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The Tatami Time Machine Blues - [AUDIOBOOK] mp3 | 186.19 MB | Author: Tomihiko Morimi | Year: 2024 Description: Category:Fiction, Literature, Literary Fiction Download Link: https://ausfile.com/yfv3cjunm85z https://rapidgator.net/file/e39d9ceec545b1989c746d54b9d207b6/

Free Download Udemy - Machine Learning - Random Forest, Adaboost & Decision Tree 2025-02-25 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English (US) | Size: 1.77 GB | Duration: 2h 58m Learn Advanced Machine Learning on Random Forest, Adaboost, Decision Trees Hands-on What you'll learn Knowing how to write a Python code for Random Forests. Implementing AdaBoost using Python. Having a solid knowledge about decision trees and how to extend it further with Random Forests. Understanding the Machine Learning main problems and how to solve them. Understanding the differences between Bagging and Boosting. Reviewing the basic terminology for any machine learning algorithm. Requirements Python basics NumPy, Matplotlib, Sci-Kit Learn Basic Probability and Statistics Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?This course is all about ensemble methods.In particular, we will study the Random Forest and AdaBoost algorithms in detail.To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.All the materials for this course are FREE. You can download and install Python, NumPy, and S[beeep] with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. Who this course is for Aspiring Data Scientists, Artificial Intelligence/Machine Learning/ Engineers, Student's/Professionals who have some basic knowledge in Machine Learning and want to know about the powerful models like Random Forest, AdaBoost, Entrepreneurs, professionals, and students who want to learn, and apply data science and machine learning to their work Homepage: https://www.udemy.com/course/random-forest-adaboost-decision-trees-in-machine-learning/ [b]AusFile[/b] https://ausfile.com/x6hhx0222sge/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part2.rar.html https://ausfile.com/yjeaf1064qr1/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part1.rar.html Rapidgator https://rg.to/file/88531164dc4d3fd1fb309b2e02fa1464/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part1.rar.html https://rg.to/file/fcb08ac42357125c5ecd783ac1586b38/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part2.rar.html Fikper Free Download https://fikper.com/8z3d32cVfy/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part2.rar.html https://fikper.com/Zebbtpptcl/ixtov.Machine.Learning.Random.Forest.Adaboost..Decision.Tree.part1.rar.html No Password - Links are Interchangeable

Free Download Udemy - AI & Machine Learning for Executives, Managers & Leaders Published: 4/2025 Created by: Infidea Trainings,Sujit Ghosh MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 39 Lectures ( 5h 18m ) | Size: 1.5 GB Unlock AI & Machine Learning for Business Success - A Practical Guide for Executives, Managers & Leaders What you'll learn Understand the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) Identify Real-World AI & ML Applications in Business Master Key AI and Machine Learning Techniques for Problem Solving Evaluate the Potential of AI in Transforming Business Operations Understand the Differences Between AI, ML, and Deep Learning Gain Practical Knowledge of AI Tools and Technologies Lead AI Integration to Drive Innovation and Growth in Your Organization Familiarize with Popular AI Tools and Platforms for Implementation Requirements No prior technical knowledge required - This course is designed for executives, managers, team leaders, and entrepreneurs, so beginners are welcome! Description Are You Ready for the AI Revolution?Artificial Intelligence (AI) is transforming industries at an unprecedented pace. From sales and marketing to finance and operations, businesses worldwide are leveraging AI to gain a competitive edge. The question is-how can YOU harness AI in your role as a leader, manager, or decision-maker?Infidea's 'Unlock AI & Machine Learning for Business Success - A Practical Guide for Executives, Managers & Leaders' course is designed to give you a clear, practical understanding of AI-without any coding! Led by industry experts, this course helps you grasp AI's impact on businesses, its real-world applications, and how you can use it to drive smarter decisions in your organization.What You'll Learn:The fundamentals of AI and how it's reshaping industriesHow AI is being used in sales, marketing, finance, and moreKey AI concepts every business leader should know (no programming required!)Practical case studies to help you apply AI insights in your workBy the end of this course, you'll have the confidence to make AI-driven strategic decisions and future-proof your career. You'll be equipped with the knowledge to identify AI opportunities in your industry and leverage data-driven insights for smarter business growth. Whether you're a manager, executive, or entrepreneur, this course will help you stay ahead in the AI-powered business landscape. Who this course is for This course is designed for executives, managers, team leaders, and entrepreneurs who want to leverage the power of Artificial Intelligence (AI) and Machine Learning (ML) in their businesses. No technical background is required-just a passion for innovation and a desire to understand how these transformative technologies can drive strategic growth, streamline operations, and improve decision-making. Whether you're leading a startup or managing a large team, this course will help you gain the knowledge and skills to make informed AI-driven decisions and lead your organization through the future of business. Ideal for: CxOs and senior executives looking to incorporate AI into their business strategy Managers and team leaders who need to understand AI's impact on operations and efficiency Entrepreneurs who want to integrate AI and ML to enhance business performance If you're ready to unlock the potential of AI and ML in your business, this course is for you! Homepage: https://www.udemy.com/course/ai-machine-learning-for-executives-managers-leaders/ [b]AusFile[/b] https://ausfile.com/3ucsmy8fzleh/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part1.rar.html https://ausfile.com/h2hdsn6ujo7w/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part2.rar.html Rapidgator https://rg.to/file/6c534513cc05b43cb080cbb756dc0ab6/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part1.rar.html https://rg.to/file/e78764bb27738d012fc02f960990146f/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part2.rar.html Fikper Free Download https://fikper.com/Y2ItF3m8km/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part2.rar.html https://fikper.com/ok6tCuWIJL/mwyzm.AI..Machine.Learning.for.Executives.Managers..Leaders.part1.rar.html No Password - Links are Interchangeable

epub | 30.77 MB | English| Isbn:1838820299 | Author: Giuseppe Bonaccorso | Year: 2020 Description: Category:Computers, Computer Programming, Programming - General & Miscellaneous AusFile RapidGator https://rapidgator.net/file/42a70a83fee9564e49bdad732084f40d/yxusj.Mastering_Machine_Learning_Algorithms_-_Second_Edition.rar TurboBit https://turbobit.net/rxseciio1cts/yxusj.Mastering_Machine_Learning_Algorithms_-_Second_Edition.rar.html https://ausfile.com/1lyfhypxcqxr/yxusj.Mastering_Machine_Learning_Algorithms_-_Second_Edition.rar

Free Download Udemy - Become an Expert in Automated Machine Learning Testing Published: 4/2025 Created by: ArkaTalent Tech MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 69 Lectures ( 4h 36m ) | Size: 1.55 GB Automate ML Testing with Python: Master Data Quality, Model Evaluation & CI/CD for Scalable AI Solutions What you'll learn Understand and apply key testing strategies, from data quality checks to model evaluation and automation Learn to write scripts for data tests, evaluation metrics, and deploy automated pipelines for robust ML testing Detect and mitigate biases in ML models with hands-on fairness testing and integration into CI/CD workflows Master metrics like MAE, F1-Score, and Silhouette for effective model evaluation across ML tasks Conduct stress tests, edge-case validations, and adversarial testing to boost model robustness Automate monitoring, detect data drift, and set up alerts to maintain model performance in production Requirements Familiarity with Python is essential as the course involves writing and automating test scripts A foundational knowledge of ML/AI concepts like models, data preprocessing, and evaluation is recommended Having some familiarity with ML libraries like Scikit-learn, TensorFlow, or PyTorch is helpful for the exercises but not essentia A willingness to explore automated workflows and testing tools is important for hands-on exercises A learning mindset to tackle challenges in testing ML models and pipelines Description Unlock the potential of your machine learning career with our comprehensive course, "Become an Expert in Automated Machine Learning Testing." This course is designed for beginners and professionals alike, providing an easy-to-follow guide into the world of ML testing and automation. You'll start with the fundamentals of machine learning, learning key concepts such as data collection, preprocessing, model training, and evaluation. This solid foundation ensures you're well-prepared to tackle more advanced topics.As you progress, you'll dive into the specifics of automated testing. You'll learn how to write test scripts to verify data quality by checking for missing values, duplicates, and outliers, ensuring your data is clean and reliable. The course also covers how to evaluate your models using essential metrics for both regression (like MAE, MSE, RMSE, R²) and classification (including Accuracy, Precision, Recall, F1-Score, ROC-AUC), giving you the skills to measure performance accurately.In addition, we cover advanced testing techniques such as cross-validation, robustness, and stress testing. You'll gain practical experience with hands-on exercises that show you how to integrate testing into your continuous integration and deployment pipelines. With insights on fairness and bias testing, as well as guidance on using popular ML libraries and automation tools, you'll be fully equipped to build scalable, secure, and high-performing ML models. Join us to transform your ML projects with industry-leading testing strategies and automation skills! Who this course is for Aspiring ML Engineers: Anyone looking to enhance their ML testing and automation skills Data Scientists: Professionals who want to ensure robust and reliable machine learning models Software Testers: Those transitioning into ML testing or seeking to expand their testing expertise Tech Enthusiasts: Learners with a passion for AI and a curiosity about testing and deployment workflows ML Developers: Engineers aiming to integrate automated testing into their machine learning pipelines Homepage: https://www.udemy.com/course/automated-ml-testing/ [b]AusFile[/b] https://ausfile.com/vs4n89ps938g/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part1.rar.html https://ausfile.com/teqn8q6ifem0/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part2.rar.html Rapidgator https://rg.to/file/44898f1b5d4b8620fb77a61ff014de68/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part1.rar.html https://rg.to/file/dfbfec44e52cfd57c4d15ad2e1222399/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part2.rar.html Fikper Free Download https://fikper.com/q77j6SrV1x/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part1.rar.html https://fikper.com/FblPeybT8I/wxlse.Become.an.Expert.in.Automated.Machine.Learning.Testing.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Master Python to Master Machine Learning Published: 4/2025 Created by: ASAN AI MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 14 Lectures ( 2h 41m ) | Size: 1.41 GB Crack Python, Unlock Machine Learning-From Scratch! What you'll learn Beginners in coding eager to explore Python for ML. Students aiming to build a strong Python foundation. Professionals transitioning into data or ML roles. Anyone curious to use Python for real-world AI tasks. Requirements Requirements No prior coding experience needed - This course is beginner-friendly and assumes no background in programming. Basic computer skills - If you can use a browser, manage files, and type, you're fully prepared to begin. A laptop or desktop with internet access - Works on Windows, Mac, or Linux. All tools used are free and installation guidance is provided. A curious mind and willingness to learn - The only true prerequisite is the motivation to explore Python and its applications in machine learning. You don't need to be a genius to learn Python. You just need consistency and the right guidance-this course gives you both. Description Course Description: Master Python to Master Machine LearningAre you excited about Artificial Intelligence and Machine Learning but don't know where to start? You're not alone-and this course is built exactly for you."Master Python to Master Machine Learning" is a beginner-friendly, future-ready course designed to take you from absolutely no coding experience to a confident Python programmer, fully prepared to take on real-world ML challenges.This course doesn't just teach you Python-it teaches you how to think in Python. You'll start with the basics like variables, data types, loops, and functions, then gradually move into working with real data, exploring libraries like NumPy, Pandas, Matplotlib, and more.But here's the best part: everything is explained in a super simple, relatable way. Whether you're a college student, working professional, or just someone curious about AI-this course welcomes you with zero jargon, real-life examples, and plenty of hands-on practice.By the end of this course, you'll not only be confident in Python, but also have a clear path toward mastering machine learning.You don't need to be a techie or a genius. You just need curiosity, consistency, and the right mentor-this course gives you all three.Let's build your future in AI, one Python line at a time. Who this course is for Who this course is for College students and graduates looking to build a strong foundation in Python for machine learning and AI careers. Working professionals and career switchers wanting to transition into data science, AI, or machine learning roles. Aspiring AI/ML enthusiasts who want to learn Python as the first step towards mastering machine learning and deep learning. Researchers and non-coders from other fields (such as finance, marketing, biology, etc.) eager to automate tasks and analyze data using Python. If you're 18 or older and curious about how machines learn and think, this course is your launchpad into the world of AI and ML. Homepage: https://www.udemy.com/course/master-python-to-master-machine-learning/ [b]AusFile[/b] https://ausfile.com/cnjhwk8s5958/uunhx.Master.Python.to.Master.Machine.Learning.part1.rar.html https://ausfile.com/u7hfyb0c0k8l/uunhx.Master.Python.to.Master.Machine.Learning.part2.rar.html Rapidgator https://rg.to/file/f85e4df37744fb71deacbd9a95414775/uunhx.Master.Python.to.Master.Machine.Learning.part1.rar.html https://rg.to/file/9c35c3a5aea762015fae2caff32aa2d5/uunhx.Master.Python.to.Master.Machine.Learning.part2.rar.html Fikper Free Download https://fikper.com/TCk4j9A4rn/uunhx.Master.Python.to.Master.Machine.Learning.part1.rar.html https://fikper.com/95kNGk4Exg/uunhx.Master.Python.to.Master.Machine.Learning.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Machine Learning A-Z - Hands-On Python & R In Data Science Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.36 GB | Duration: 0h 46m Master Machine Learning with Python & R: A Practical, Hands-On Guide What you'll learn Master Machine Learning using Python & R Implement models from scratch using real datasets Learn Regression, Classification, Clustering & NLP Master Boosting, Neural Networks & Deep Learning Work on real-world projects for portfolio building Requirements Basic Math & Statistics - Mean, Variance, Probability, and Linear Algebra basics Basic Programming - Some knowledge of Python and/or R will be beneficial Logic & Problem-Solving Skills - Understanding algorithms and logical thinking Description Machine Learning A-Z: Hands-On Python & R in Data Science is a comprehensive course designed to take you from beginner to expert in machine learning. This hands-on course covers the most important machine learning techniques, including regression, classification, clustering, reinforcement learning, and deep learning, using both Python and R.You'll gain practical experience by working on real-world datasets and building predictive models step by step. With clear explanations, coding exercises, and intuitive visualizations, this course ensures you understand both the theory and application of machine learning.What You'll Learn:Supervised & Unsupervised Learning techniquesRegression, Classification, and Clustering modelsNeural Networks and Deep Learning fundamentalsFeature Engineering and Model EvaluationImplementing ML algorithms in Python and RWhether you're a beginner or looking to sharpen your skills, this course will give you the confidence to apply machine learning to real-world problems and advance your career in data science.About the OrganizationEntrepreneurs Network and Business Advancement is a dynamic platform designed to connect, support, and empower entrepreneurs, business owners, and professionals. This network fosters collaboration, innovation, and growth by providing valuable resources, mentorship, and networking opportunities.What We Offer:Networking Opportunities - Connect with like-minded entrepreneurs, investors, and industry leaders.Business Growth Strategies - Gain insights into scaling your business, marketing, and financial planning.Workshops & Events - Attend expert-led sessions on leadership, innovation, and emerging business trends.Mentorship & Coaching - Learn from experienced business professionals to navigate challenges and accelerate success.Funding & Investment Support - Access information on venture capital, grants, and business funding options.Whether you are a startup founder, small business owner, or an aspiring entrepreneur, Entrepreneurs Network and Business Advancement provides the tools, connections, and knowledge to help you thrive in today's competitive business landscape. Absolute beginners interested in Machine Learning,Data Analysts looking to upskill in AI/ML,Software Developers transitioning into Data Science,Business Professionals who want to apply ML in their domain,Students & Researchers needing ML knowledge for projects Homepage: https://www.udemy.com/course/machine-learning-a-z-hands-on-python-r-in-data-science/ [b]AusFile[/b] https://ausfile.com/7k86zydorjpm/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part1.rar.html https://ausfile.com/z36b4ax0a72t/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part2.rar.html Rapidgator https://rg.to/file/e603283faf0b6b62a7bd17d940f9926a/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part1.rar.html https://rg.to/file/38d7a1c50606fb76750e9c4e41e64fdd/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part2.rar.html Fikper Free Download https://fikper.com/yYrzYxNX1c/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part1.rar.html https://fikper.com/XJTnLKoiP4/gwxkv.Machine.Learning.AZ.HandsOn.Python..R.In.Data.Science.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Machine Learning (ML) Methods In Petroleum Industry Seminar Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 358.66 MB | Duration: 1h 14m This AL/ML Focused Seminar Presented by Sr. Petroleum Engineering Data Consultant What you'll learn Machine Learning Overview Descriptive Statistics Regression Classification Clustering Time Series forecasting Requirements Interest in Oil and Gas Drilling Engineering Passion to Learn Artificial intelligence (AI) and machine learning (ML) Description This seminar, presented by Sr. Petroleum Engineering Data Consultant, which covers a broad Overview of machine learning concepts and their application within the oil and gas sector. It starts with the definition of machine learning (ML), emphasizing its ability to learn from data without explicit programming. The presentation highlights the wide range of ML applications, from image and speech recognition to fraud detection and financial forecasting, with following agenda: Introduction to Machine LearningDescriptive StatisticsRegressionClassificationClusteringTime Series forecastingThe core of the presentation focuses on key ML techniques: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is detailed with methods like linear regression, logistic regression, support vector machines (SVM), decision trees, and ensemble techniques. Unsupervised learning is highlighted with K-Means clustering, hierarchical clustering, and dimensionality reduction. Feature engineering and selection are discussed as critical steps in the ML workflow, involving the creation of new features from existing data and the identification of the most relevant features for model building.Descriptive statistics are presented as essential for understanding data, using P-values and correlation coefficients to determine significance and relationships between variables. The presentation outlines data types as qualitative (attributes) and quantitative (categorical). A significant portion is dedicated to regression analysis, including linear, multiple linear, and non-linear regression models. Specific applications in the petroleum industry are highlighted, which are including seismic interpretation, reservoir characterization, PVT modelling, etc.Finally, the presentation covers time series forecasting using statistical, machine learning, and deep learning methods. Statistical methods such as Moving Average are talked about with more advanced Machine Learning Methods such as Random Forest, ending with Deep Learning techniques like Recurrent Neural Networks. Geologist, Petroleum Engineers, Oil and Gas Employees,Petrophysicist, Geoscientist, Cased Hole Logs Analysts and Interpreters,Geology and Petroleum Engineering College and University Students,Python ,Artificial intelligence (AI) and Machine learning (ML) Enthusiast,Workover and Drilling Professionals Homepage: https://www.udemy.com/course/machine-learning-ml-methods-in-petroleum-industry-seminar/ [b]AusFile[/b] https://ausfile.com/lesg0jlxqace/gyknk.Machine.Learning.ML.Methods.In.Petroleum.Industry.Seminar.rar.html Rapidgator https://rg.to/file/db87d0f64e6e633ae7dcdfb9abc54660/gyknk.Machine.Learning.ML.Methods.In.Petroleum.Industry.Seminar.rar.html Fikper Free Download https://fikper.com/oW4H4AAiox/gyknk.Machine.Learning.ML.Methods.In.Petroleum.Industry.Seminar.rar.html No Password - Links are Interchangeable

Free Download Udemy - Robotics & Mechatronics 1 - Machine Theory & Production Lines Last updated: 11/2023 Created by: Mouhammad Hamsho,Kemalaldin Hamso MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + Embedded subtitle | Duration: 55 Lectures ( 4h 50m ) | Size: 3.68 GB Pure Basic Mechanical Theory of Industrial Production Line | Conveyors, Part Feeders, Linear Guides, Vibration Feeders What you'll learn Lean How Real-World Industrial Machines are Designed Understanding The Mechanical Parts involved in Machine Design Learn about Shafts, Bearings, Belts Pulleys and See where they are used Learn About Pick and Place Machines and Linear Guides Design Learn about Conveyor Systems in Details Learn about Parts Feeding Systems Learn about Vibrational And Linear Systems Learn about End Effectors and Robot Grippers Requirements No Prior Knowledge is Required Description This first Course of the series, is Purely about Mechanical Machine Design. You cannot apply Machine & Industrial Automation Control And Monitoring, without having a Machine to control in the first place!That's what this course is about, getting you introduced to :1. What are all of those moving parts we see in Industrial Machines2. What types of production lines can those parts actually make.Welcome to you in the first course of the five Courses Series about Robotics, Mechatronics and Industrial Automation.In this first Theoretical course you will learn about:Shafts, Pulleys, Gears, Belts, Bearings, and all of those moving partsSizing Machine Motors according to the mechanical loadMachine manufacturing materials like Stainless steel and PlasticsConveyor systems design theory and Conveyor typesSingle/Three Axis Linear Motion systems design theoryStorage systems and DischargeFeeding systems in assembly lines and their TypesEnd Effectors for Milling and Pick and place applicationsMost famous industrial RoboticsAlgorithm used to Control Industrial RoboticsA sum it all study caseAnd tons of Quizzes!Why should you learn Machine Theory and Industrial Design?Tons and tons of tutorials are out there teaching about Control, Electronics and Machine Programming. But the courses actually talking about the bones of all of this is almost never existence. I'm here to introduce you to the basics of Mechanical Systems used In Industrial and Manufacturing Environment.What to expect after completing this Course?Have a foundation about mechanical partsBe able to brain storm to design new machineUnderstand the different types of Production LinesBe able to identify production line and parts by sightThis is course #1/5 in the Robotics & Mechatronics Series. The purpose of this series is to be able to design machines, control them, Digital Twin them, and then actually build them! Who this course is for Industrial Automation Engineers Mechanical Designers Robotics Engineers Product Designers Machine Design Engineers Homepage: https://www.udemy.com/course/robotics-mechatronics-1-machine-design-theory/ [b]AusFile[/b] https://ausfile.com/dz4fnwm5bril/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part1.rar.html https://ausfile.com/pk88wair5arj/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part2.rar.html https://ausfile.com/asgwklkv07ik/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part3.rar.html https://ausfile.com/oy6c21gjy3fm/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part4.rar.html Rapidgator https://rg.to/file/5a78ee58e78c272a067a00a371551b22/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part1.rar.html https://rg.to/file/107c694fc5ef646d707d7ed323724c47/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part2.rar.html https://rg.to/file/bb5abd4de39064f0051f8d2dfb3e4ef5/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part3.rar.html https://rg.to/file/d0ad1b4029bbe123c463e89718833fb4/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part4.rar.html Fikper Free Download https://fikper.com/RNB2wzQ3FF/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part1.rar.html https://fikper.com/dimtdPQKdK/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part2.rar.html https://fikper.com/SqvQuPyUeV/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part3.rar.html https://fikper.com/uYiA6MzUTW/ugcad.Robotics..Mechatronics.1.Machine.Theory..Production.Lines.part4.rar.html No Password - Links are Interchangeable

Free Download The Ultimate AI And Machine Learning Foundations For Leaders Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.68 GB | Duration: 2h 23m Master AI and Machine Learning Fundamentals and see its application in Business and Operations. What you'll learn Demonstrate a solid understanding of the difference between AI, Machine Learning and Deep Learning. Clearly articulate why Large Language Models like ChatGPT and Bard are NOT intelligent. Articulate the difference between Supervised, Unsupervised, and Reinforcement Machine Learning. Explain the concept of machine learning and its relation to AI. Describe what Artificial Intelligence is, and what it is not. Explain what types of sophisticated software systems are not AI systems. Describe how Machine Learning is different to the classical software development approach. Compare and contrast supervised, unsupervised, and reinforcement learning. Explain Supervised and Unsupervised Machine Learning terms such as algorithms, models, labels and features. Explain Function Approximators and the role of Neural Networks as Universal Function Approximators. Explain Encoding and Decoding when using machine learning models to work with non-numeric, categorical type data. Demonstrate an intuitive understanding of Reinforcement Learning concepts such as agents, environments, rewards and goals. Apply basic principles of neural networks to a hypothetical problem. Discuss the role of data in training AI models Construct a neural network model for a specified task Understand the use of AI and Reinforcement Learning in Personalized Recommender Systems Requirements High school Math and a deep interest in machine learning would be highly beneficial for this series of lessons. There is no coding or complex mathematics involved in this course. If you can't remember high-school math, it will not prevent you from learning the concepts in this course. Basic computer literacy, including familiarity with operating a computer. Description Conquer the Future: Master the Realms of AI and ML!Welcome to an extraordinary journey into the realms of Artificial Intelligence and Machine Learning. Led by AI and Technology expert Irlon Terblanche, this course is not just an educational experience; it's an adventure into the technologies shaping our future. Whether you're a curious beginner, a business leader, or an aspiring tech guru, this course promises to transform your understanding of some of the most cutting-edge topics in tech.Why This Course?Designed for Curiosity and Career: Tailored for both personal and professional growth, this course demystifies AI and ML, making them accessible to everyone. It's perfect for busy professionals, entrepreneurs, and anyone with a thirst for knowledge.No Math Fears: We've designed the course to be inclusive, requiring no prior expertise in math or coding. It's all about understanding concepts in a friendly, approachable manner.The Best Foundations for AI & Machine Learning: Complex topics are carefully explained, and built on top of previously-explained concepts.Lifetime Access and Flexible Learning: Learn at your pace with full lifetime access to all resources, including videos, articles, and downloadable materials.What You'll Achieve:Grasp the Core Concepts: Understand the difference between AI, ML, and Deep Learning. Learn what sets them apart and how they're revolutionizing industries.Understand more than just the basics: Understand the fundamental differences between Supervised, Unsupervised and Reinforcement Learning.See Real-World Applications: See how AI and ML are being applied in various sectors, including but not limited to its application to personalized recommender systems.Course Highlights:Engaging Video Lectures: Over 2 hours of high-quality, engaging video content that breaks down complex ideas into digestible segments.Comprehensive Topics: From the basics of neural networks to the intricacies of supervised, unsupervised and reinforcement learning.Practical Demonstrations: See real-world business applications of AI.Mobile and PC Access: Learn on the go or from the comfort of your living room.Enrol Now and Transform Your Understanding of AI and ML!Join us on this captivating journey into AI and ML. With Irlon Terblanche's expert guidance, engaging content, and practical insights, you're not just learning; you're preparing for the future. Enroll today and be part of the AI revolution! Business Executives and Managers: Professionals in leadership roles who are looking to understand how AI can be leveraged for strategic advantage in their organizations.,Busy professionals who need a short, easy but solid understanding of AI fundamentals.,Entrepreneurs and Startup Founders: Individuals who are building or planning to build businesses where AI could play a transformative role.,Technology Consultants and Advisors: Professionals who provide strategic advice on technology adoption and integration.,Absolute beginners who are aspiring to become Data Scientists or Machine Learning Engineers, and who are looking for the best fundamentals of artificial intelligence and machine learning.,Product Managers and Developers: Those who are involved in product development and are interested in incorporating AI into new or existing products.,Non-technical Professionals: Including, but not limite to Business Analysts or Marketers. Yhis course can give you all the skills you need to be able to interact with Data Scientists, Machine Learning Engineers or other AI specialists.,AI and machine learning enthusiasts: This course will still be valuable because it covers extremely important fundamental concepts that are often misunderstood. 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Free Download Data-Centric Machine Learning with Python - Hands-On Guide Published: 3/2025 Created by: Meta Brains,Skool of AI MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 37 Lectures ( 3h 35m ) | Size: 2 GB Master data preprocessing, feature engineering, and ML modeling techniques with a hands-on loan prediction project. What you'll learn Preprocess data effectively for machine learning models. Perform exploratory data analysis using Python libraries. Differentiate between supervised and unsupervised learning. Build and optimize machine learning algorithms in Python. Create insightful data visualizations and plots. Apply feature engineering techniques to improve models. Evaluate model performance with appropriate metrics. Solve real-world problems using machine learning workflows. Requirements Basic understanding of Python programming. Familiarity with high school-level math concepts. A computer with Python and necessary libraries installed. No prior data science or machine learning knowledge needed! Description In a world where data is the new oil, mastering machine learning isn't just about algorithms-it's about understanding the data that fuels them.This intensive 3-4 hour course dives deep into the data-centric approach to machine learning using Python, equipping parti[beeep]nts with both theoretical knowledge and practical skills to extract meaningful insights from complex datasets. The curriculum focuses on the critical relationship between data quality and model performance, emphasizing that even the most sophisticated algorithms are only as good as the data they're trained on.Parti[beeep]nts will embark on a comprehensive learning journey spanning from foundational concepts to advanced techniques. Beginning with an introduction to machine learning paradigms and Python's powerful data science ecosystem, the course progresses through the crucial stages of data preparation-including exploratory analysis, handling missing values, feature engineering, and preprocessing. Students will gain hands-on experience with supervised learning techniques, mastering both regression and classification approaches while learning to select appropriate evaluation metrics for different problem types.The course extends beyond basic applications to cover sophisticated model selection and validation techniques, including cross-validation and hyperparameter tuning, ensuring models are robust and generalizable. Unsupervised learning methods such as clustering and anomaly detection further expand parti[beeep]nts' analytical toolkit, while specialized topics like text analysis, image classification, and recommendation systems provide insight into real-world applications.The learning experience culminates in a practical loan prediction project where parti[beeep]nts apply their newly acquired knowledge to develop a predictive model for loan approvals based on applicant information-bridging theoretical understanding with practical implementation. Through this hands-on approach, students will develop the critical thinking skills necessary to tackle complex machine learning challenges in various professional contexts, making this course ideal for aspiring data scientists, analysts, and technology professionals seeking to leverage the power of data-centric machine learning.Don't wait! Transform your career with this focused course that delivers in hours what others learn in months. With companies actively seeking data-centric ML skills, secure your spot now to gain the competitive edge that commands premium salaries. Your future in data science starts here! Who this course is for Beginners looking to explore machine learning concepts. Python programmers wanting to expand their skill set. Data analysts eager to transition into machine learning. Students interested in practical applications of data science. Professionals seeking to automate data-driven decision-making. Enthusiasts curious about building predictive models. 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Free Download Udemy - Data Preprocessing for Machine Learning and Data Analysis Published: 3/2025 Created by: Muhtar Qong MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 28 Lectures ( 8h 19m ) | Size: 4.35 GB A Comprehensive Guide for AI & Machine Learning Developers and Data Scientists What you'll learn Understand the importance of high-quality data in AI & machine learning. Apply data cleaning techniques to handle missing and poor-quality data. Perform feature selection, scaling, and transformation for better model performance. Work with categorical, numerical, text-based, and image features effectively. Identify correlations and use visualization techniques to gain insights. Implement Prin[beeep]l Component Analysis (PCA) for dimensionality reduction. Properly split datasets for training, testing, and cross-validation. Build automated data preprocessing pipelines using custom transformers. Visualize data using weighted scatter plots and shapefiles. Understand and process image and geographic datasets for AI & machine learning applications. Gain experience with traditional structured datasets, image datasets, and geographic datasets, providing a broader perspective on data used in AI & ML projects. Enhance your resume with in-demand data science skills, including statistical analysis, Python with NumPy, pandas, Matplotlib and advanced statistical analysis. Learn and apply useful data preprocessing techniques using Scikit-learn, pandas, NumPy, and Matplotlib. Requirements There are no special Requirements for this course. If you have beginner to intermediate-level Python experience, that is enough to follow along and understand the concepts. This course follows a classic classroom-style approach, where we first cover the theoretical foundations before moving on to hands-on coding sessions. This structured format makes the course easy to understand for learners at all levels. Description This course includes 29 downloadable files, including one PDF file containing the entire course summary (91 pages) and 28 Python code files attached to their corresponding lectures.If we understand a concept well theoretically, only then can we apply it effectively for our purposes. Therefore, this course is structured in a classic "classroom-style" approach. First, we dedicate sufficient time to explaining the theoretical foundations of each topic, including why we use a particular technique, where it is applicable, and its advantages.After establishing a solid theoretical understanding, we move on to the coding session, where we explain the example code line by line. This course includes numerous Python-based coding examples, and for some topics, we provide multiple examples to reinforce understanding. These examples are adaptable, meaning you can modify them slightly to fit your specific projects.Data preprocessing is a crucial step in AI and machine learning, directly affecting model performance, accuracy, and efficiency. Since raw data is often messy and unstructured, preprocessing ensures clean, optimized datasets for better predictions.This hands-on course covers essential techniques, including handling missing values, scaling, encoding categorical data, feature engineering, and dimensionality reduction (PCA). We will also explore data visualization with geographic information, weighted scatter plots, and shapefiles, particularly useful for geospatial AI applications.Beyond traditional structured datasets, this course includes image and geographic datasets, giving learners a broader perspective on real-world AI projects.By the end, you'll be able to build automated data preprocessing pipelines and prepare datasets efficiently for machine learning and deep learning applications.Ideal for ML engineers, data scientists, AI developers, and researchers, this course equips you with practical skills and best practices for high-quality, well-processed datasets that enhance model performance. You can download the entire course summary PDF from the final lecture (Lecture 28) Who this course is for Aspiring AI & Machine Learning Developers who want to master data preprocessing. Data Scientists & Analysts looking to improve model accuracy and efficiency. AI & ML Engineers working with real-world datasets, including geographic and image data. Students & Researchers interested in learning advanced data preparation techniques. 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Free Download Building Recommendation Engine With Machine Learning & Rag Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.72 GB | Duration: 3h 21m Learn how to build product, movie, music recommendation engines using Tensorflow, Keras, Surprise, SVD, and RAG What you'll learn Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, RAG implementation in recommendation system Learn how to build product recommendation engine using Tensorflow and Keras Learn how to build movie recommendation engine using Surprise Learn how to build music recommendation engine using retrieval augmented generation Learn how to build product recommendation engine using TFIDF Vectorizer and Cosine Similarity Learn how to build search based recommendation engine using RAG Learn how recommendation engines work. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deployment Learn how to perform feature selection for product recommendation engine Learn how to perform feature selection for movie recommendation engine Learn how to build and train collaborative filtering model Learn how to load RAG model and create Facebook AI Similarity Search index Learn how to build user interface for recommendation engine using Gradio and Streamlit Learn how to test and deploy recommendation engine on Hugging Face Learn how to download dataset using Kaggle Hub API Requirements No previous experience in machine learning is required Basic knowledge in Python Description Welcome to Building Recommendation Engine with Machine Learning & RAG course. This is a comprehensive project based course where you will learn how to build intelligent recommendation systems using Tensorflow, Surprise and Retrieval Augmented Generation. This course is a perfect combination between Python and machine learning, making it an ideal opportunity to level up your programming skills while improving your technical knowledge in software development. In the introduction session, you will learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and also learn how retrieval augmented generation can be used to improve your recommendation system. Then, in the next section, you will learn step by step how a recommendation engine works. This section covers data collection, data preprocessing, feature selection, model selection, model training, model evaluation, deployment, monitoring, and maintenance. Afterward, you will also learn how to find and download datasets from Kaggle, it is a platform that offers many high quality datasets from various industries. Once everything is ready, we will start the project. Firstly, we are going to build a product recommendation engine using TensorFlow, it will have the capability of suggesting relevant products to users based on their browsing and purchase history. This recommendation engine will be able to analyze user behavior, extract meaningful patterns, and generate personalized product recommendations in real time. By implementing this system, businesses can enhance customer engagement, increase conversion rates, and optimize the shopping experience through intelligent suggestions. In the next section, we are going to build a movie recommendation engine using Surprise, which will help users discover films they might enjoy based on their past ratings and preferences. This recommendation engine will utilize collaborative filtering techniques to find similarities between users and movies, delivering highly personalized recommendations. With this approach, we can improve content discovery, keep users engaged, and drive higher retention rates for streaming platforms. Following that, we are also going to build a music recommendation engine using Retrieval Augmented Generation that is able to provide dynamic and context aware song recommendations. This recommendation engine will be able to enhance traditional recommendation methods by incorporating real-time external knowledge, improving the accuracy and diversity of song suggestions. Lastly, at the end of the course, we will conduct testing to evaluate the performance of our recommendation engines. After ensuring optimal model performance, we will deploy the recommendation system to Hugging Face Space, where users can select a few initial movies as input, allowing the model to process real-time data and generate personalized recommendations based on learned patterns and similarities.Before getting into the course, we need to ask this question to ourselves, why should we build a recommendation engine using machine learning? Well, here is my answer, by leveraging machine learning, businesses can offer smarter, more personalized recommendations that keep customers engaged, increase sales, and improve loyalty. Meanwhile, from users perspective, they can benefit from a seamless experience, where they receive valuable recommendations effortlessly, saving time and effort in finding what suits their needs.Below are things that you can expect to learn from this course:Learn the basic fundamentals of recommendation engine, such as getting to know its use cases, technical limitations, and RAG implementation in recommendation systemLearn how recommendation engines work. This section cover, data collection, preprocessing, feature selection, model training, model evaluation, and deploymentLearn how to download dataset using Kaggle Hub APILearn how to perform feature selection for product recommendation engineLearn how to build product recommendation engine using Tensorflow and KerasLearn how to build product recommendation engine using TFIDF Vectorizer and Cosine SimilarityLearn how to perform feature selection for movie recommendation engineLearn how to build movie recommendation engine using SurpriseLearn how to build and train collaborative filtering modelLearn how to build music recommendation engine using retrieval augmented generationLearn how to load RAG model and create Facebook AI Similarity Search indexLearn how to build search based recommendation engine using RAGLearn how to build user interface for recommendation engine using Gradio and StreamlitLearn how to test and deploy recommendation engine on Hugging Face Software engineers who are interested in building recommendation engine using machine learning,Data scientists who are interested in transforming customer data into relevant product recommendations Homepage: https://www.udemy.com/course/building-recommendation-engine-with-machine-learning-rag/ [b]AusFile[/b] https://ausfile.com/6km5cel8vplo/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://ausfile.com/2aecmc10y8cf/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html Rapidgator https://rg.to/file/bbd14b547dd77a01b4e7e7b0dfbf60a4/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://rg.to/file/d07abbcf7256d5cf42f80a1b783b185d/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html Fikper Free Download https://fikper.com/4knwY1Q6Pl/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part1.rar.html https://fikper.com/arYoJYvJuL/llxxk.Building.Recommendation.Engine.With.Machine.Learning..Rag.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Machine Learning & Deep Learning Masterclass In One Semester Last updated: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 16.94 GB | Duration: 46h 49m Practical Oriented Explanations by solving more than 80 projects with NumPy, Scikit-learn, Pandas, Matplotlib, PyTorch. What you'll learn Theory, Maths and Implementation of machine learning and deep learning algorithms. Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest Build Artificial Neural Networks and use them for Regression and Classification Problems Using GPU with Neural Networks and Deep Learning Models. Convolutional Neural Networks Transfer Learning Recurrent Neural Networks and LSTM Time series forecasting and classification. Autoencoders Generative Adversarial Networks (GANs) Python from scratch Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries. More than 80 projects solved with Machine Learning and Deep Learning models Requirements Some Programming Knowledge is preferable but not necessary Gmail account ( For Google Colab ) Description IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrin[beeep]l Component Analysis (PCA)What you'll learnTheory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scratchNumpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.More than 80 projects solved with Machine Learning and Deep Learning models. 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Explore deep learning, neural networks, generative models, and advanced topics like meta-learning, federated learning, and graph neural networks through real-wo Discover how to deploy machine learning models, optimize performance with distributed computing, and integrate AI solutions into applications. Requirements Familiarity with Python programming, including data types, control structures, and functions. A basic understanding of algebra, calculus, and statistics to grasp algorithmic concepts. Prior exposure to simple ML concepts or courses can be beneficial, though not mandatory for beginners. Working knowledge of libraries like NumPy and Pandas for data manipulation and analysis. A proactive attitude toward solving problems, experimenting with code, and building projects. Description Embark on a transformative journey into the world of Machine Learning and Artificial Intelligence with our comprehensive online course. 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Free Download Udemy - Advanced Machine Learning Methods and Techniques Published: 3/2025 Created by: Henrik Johansson MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English + subtitle | Duration: 18 Lectures ( 11h 15m ) | Size: 4.86 GB Learn Advanced Machine Learning Methods and Techniques for Data Analysis, Data Science, and Machine Learning What you'll learn Knowledge about Advanced Machine Learning methods, techniques, theory, best practices, and tasks Deep hands-on knowledge of Advanced Machine Learning and know how to handle Machine Learning tasks with confidence Advanced ensemble models such as the XGBoost models for prediction and classification Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, and Supervised Learning Hands-on knowledge of Scikit-learn, Matplotlib, Seaborn, and some other Python libraries Advanced knowledge of A.I. prediction/classification models and automatic model creation Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources And much more. Requirements The four ways of counting (+-*/) Some Experience with Data Science, or Data Analysis, or Machine Learning Python and preferably Pandas knowledge Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended Access to a computer with an internet connection The course only uses costless software Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included Description Welcome to the course Advanced Machine Learning Methods and Techniques!Machine Learning is expanding and developing on a massive and global scale. 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Free Download Udemy - Mathematics For Machine Learning by Daniel Yoo Published: 3/2025 Created by: Daniel Yoo MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 40 Lectures ( 9h 3m ) | Size: 3.9 GB The math you will need for your machine learning journey. What you'll learn People who want to learn the mathematics that drives machine learning models. Students who are not sure about data science as a career and want to give it a serious try without paying college level tuition Data scientists who want a refresher in mathematics. Students who want to have a solid foundation in mathematics to proceed to more advanced machine learning models. Product managers who want to know how data scientists and machine learning engineers think. Machine Learning Engineers, who know how to deploy models, but want to know what is actually going underneath the hood of these models. Requirements No programming or math experience necessary, foundational concepts are developed from scratch. Description This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition-critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.Throughout the course, students will gain hands-on experience with NumPy and S[beeep], leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling. Who this course is for Anybody who wants to understand the mathematics behind machine learning models. Students, who are not sure if data science is a viable career for them. Homepage: https://www.udemy.com/course/mathematics-for-machine-learning-o/ Rapidgator https://rg.to/file/0140294f45eaa49a507a33440d1792ab/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part2.rar.html https://rg.to/file/0412c1971050a07388c5ca434af90557/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part5.rar.html https://rg.to/file/60bad2cf528362906c1461b5774bebbe/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part4.rar.html https://rg.to/file/629ee60aece12d43886de3d09aac995f/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part3.rar.html https://rg.to/file/672b832e589cd7b97975f067ee9a61d5/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part1.rar.html Fikper Free Download https://fikper.com/ADCEhDRpnG/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part5.rar.html https://fikper.com/VoZ7UxJ58F/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part3.rar.html https://fikper.com/hEhqtFtn55/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part4.rar.html https://fikper.com/mee5t4T8Zv/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part1.rar.html https://fikper.com/n9XtIZX60u/epnpc.Mathematics.For.Machine.Learning.by.Daniel.Yoo.part2.rar.html No Password - Links are Interchangeable

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Free Download Build a Full-Stack Machine Learning Web App In Production Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 4m | Size: 2.35 GB Build an AI document search web app with Flask and deploy it to production What you'll learn Become a Full-Stack AI/ML Engineer Build complex Flask web applications and websites Train BERT-like Deep Learning Models and deploy as an API Design distributed computing workloads with Celery and Redis Gain proficiency with Databases using PostgreSQL and SQLAlchemy Deploy websites to production with Railway Enhance your job portfolio, freelance work or even start your own SaaS Requirements A computer running Windows, OSX or Linux with at least 8GB of RAM Basic understanding of HTML, CSS and JavaScript Basic understanding of computer science and AI Description Build a Full-Stack ML Web App: From Model to ProductionAre you ready to become a highly-paid Machine Learning Engineer in today's AI revolution?Hi, I'm Dylan P., and as a Lead Machine Learning Engineer with over 5 years of experience at major tech companies, I've watched ML Engineering become the hottest job in tech. 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The techniques in this course are battle-tested from my years building production ML systems:Use industry best practices and tools like Docker, Databases, Caching, Distributed Computing, Unit / Integration TestingSystem design that allows your app to scale up to thousands of users without breakingUtilize cutting-edge models from traditional ML to state-of-the-art Transformers and LLMsDeliver measurable business impact while optimizing cost and performance"This course provides exactly what I needed - not just theory, but practical implementation that translates directly to my work projects." - James WongHere's What you'll learn by taking my course:Full-Stack Development: Create both the front end and backend with Flask, Docker, and RedisML System Design: How to design an AI web app that can scale effectively Natural Language Processing: Train a BERT language model from scratch using PyTorch, Hugging Face, WandbProduction-Grade APIs: Turn an AI model into high performance APIs with FastAPIDatabase Integration: Connect your app with production databases with PostgreSQLDeployment Mastery: Take your application live using RailwayThe best part? By the end of this course, you'll have a complete, impressive project for your portfolio that demonstrates exactly the skills employers are desperately seeking.Who is this course for?Software engineers looking to transition into the lucrative field of ML engineeringData scientists who want to level up by learning deployment and production skillsCS students or mid career switchers who want to build up their portfolioFreelance Consultants/Entrepreneurs keen in creating their own ML-powered applications or SaaS products"I was stuck in data science theory for years. After this course, I finally know how to build end-to-end ML systems that actually solve real problems." - Emery LinCourse StructureEach chapter follows a hands-on approach:Learn: Clear slides introducing new concepts and technologiesWatch: Video walkthroughs of actual code implementationBuild: Hands-on coding to construct your applicationVisualize: See your results in actionChallenge: Chapter exercises to cement your understandingInvest in Your Future The skills taught in this course regularly command $120,000-$180,000+ salaries in the industry. As AI continues transforming every sector, these skills will only become more valuable.Don't waste months piecing together fragmented tutorials or building projects that don't reflect real-world Requirements. Join me, and in just a few weeks, you'll have mastered the complete skillset needed to thrive as a modern ML Engineer.Ready to become the ML Engineer companies are looking to hire? Enroll now and start building your first production-ready ML web application today! Who this course is for Software Engineers looking to learn how to build production-ready apps with AI Aspiring SaaS Founders who want to build AI-powered web applications Freelancers learning to expand their skillset with AI web development Tech industry professionals or mid-career switchers looking to upskill themselves Homepage: https://www.udemy.com/course/build-a-full-stack-machine-learning-web-app-in-production/ Rapidgator https://rg.to/file/423e92e15393a9b86d13dadf2b98fc51/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part1.rar.html https://rg.to/file/de612a8353ebe6832940568c2bb9de56/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part2.rar.html https://rg.to/file/e10fda6542d2d7063f98e025f7a945c0/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part3.rar.html Fikper Free Download https://fikper.com/S7ImQrkFil/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part1.rar.html https://fikper.com/5KrpGzg98k/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part2.rar.html https://fikper.com/gj9CsUe2gF/pyeos.Build.a.FullStack.Machine.Learning.Web.App.In.Production.part3.rar.html No Password - Links are Interchangeable

Free Download Machine Learning and AI in Cybersecurity by Chuck Easttom Released 3/2025 By Chuck Easttom MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 3h 8m | Size: 648 MB Course Outline Machine Learning and AI for Cybersecurity: Introduction Learning objectives 1.1 Current Status of Machine Learning for Cyber Security 1.2 Basics of Machine Learning 1.3 Data Mining Basics Learning objectives 2.1 Defensive Uses of Machine Learning 2.2 Offensive Uses of Machine Learning Learning objectives 3.1 TensorFlow Basics 3.2 More with TensorFlow 3.3 TensorFlow Issues 3.4 Neural Networks with TensorFlow Learning objectives 4.1 What Are Large Language Models? 4.2 ChatGPT and Alternatives 4.3 Deep Fakes Learning objectives 5.1 Defining Cyber Warfare 5.2 Weaponized Malware Learning objectives 6.1 Neural Network Variations 6.2 Clustering Algorithms Machine Learning and AI for Cybersecurity: Summary Rapidgator https://rg.to/file/c8999d2da084e68825c13c9fe454f8da/ypguk.Machine.Learning.and.AI.in.Cybersecurity.rar.html Fikper Free Download https://fikper.com/3u9bEfWuXN/ypguk.Machine.Learning.and.AI.in.Cybersecurity.rar.html No Password - Links are Interchangeable

Free Download The Complete Azure Machine Learning Course - 2025 Edition Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.24 GB | Duration: 4h 19m Master Machine Learning with Azure ML Studio - Build, Train & Deploy AI Models Using No-Code & Python. What you'll learn Learn about supervised, unsupervised, and reinforcement learning, key concepts like training data, models, predictions, and real-world applications. Navigate and utilize Azure ML Studio's tools, including Designer, Notebooks, Automated ML, and Model Management. Load, clean, transform, and engineer features using Azure ML Studio to optimize model performance. Use Azure ML Studio's visual interface and custom Python scripts to create, train, and evaluate machine learning models. Apply hyperparameter tuning, cross-validation, and automated ML techniques to enhance model accuracy and efficiency. Learn different model deployment strategies, including real-time inference, batch inference, and Edge deployments using Azure Kubernetes Service (AKS) and Azure Create reusable machine learning workflows using Azure ML Pipelines for training, evaluation, and deployment automation. Set up CI/CD pipelines, automate model retraining, monitor model drift, and ensure security and compliance with Azure DevOps. Work with GPT, DALL·E, Stable Diffusion, and Codex, fine-tune AI models, and apply responsible AI principles for fairness and transparency. Work through multiple demos, labs, and real-world projects to gain practical experience in Azure Machine Learning. Requirements Familiarity with Python syntax, data types, and simple programming concepts will be helpful but is not mandatory. Some awareness of cloud services, particularly Microsoft Azure, will be useful but not required. Concepts like averages, probability, and basic algebra will help in understanding machine learning models, but the course will explain these as needed. You'll need an Azure account to access Azure Machine Learning Studio and complete hands-on exercises. Since Azure ML Studio is cloud-based, you'll need a stable internet connection. The course runs entirely in Azure Machine Learning Studio, so no local installations are needed. If you don't have an Azure account, you can sign up for a free tier to access cloud-based ML tools. Description Machine learning is revolutionizing industries by enabling data-driven decision-making and automation. However, implementing machine learning models can be complex, requiring infrastructure setup, data processing, and model deployment. Microsoft Azure Machine Learning Studio simplifies this process by providing a cloud-based platform to build, train, and deploy machine learning models efficiently. This course is designed to help learners master Azure ML Studio through a structured, hands-on approach.This course covers the entire machine learning lifecycle, from understanding key concepts to deploying models in production environments. Learners will explore:Types of Machine Learning - Supervised, unsupervised, and reinforcement learning.Real-world applications in healthcare, finance, cybersecurity, and retail.Challenges in Machine Learning - Overfitting, data quality, interpretability, and scalability.Hands-on with Azure ML StudioThrough practical demonstrations, learners will:Navigate the Azure Machine Learning Studio interface and set up a workspace.Manage datasets, experiments, and models in a cloud-based environment.Preprocess data - Handle missing values, perform feature engineering, and split datasets for training.Use data transformation techniques - Standardization, normalization, one-hot encoding, and PCA.Building & Training Machine Learning ModelsLearners will explore different machine learning algorithms and techniques, including:Regression, classification, and clustering models in Azure ML Studio.Feature selection and hyperparameter tuning for better model performance.AutoML (Automated Machine Learning) for optimizing models with minimal effort.Ensemble learning methods such as Random Forests, Gradient Boosting, and Neural Networks.Model Deployment & OptimizationOnce models are trained, learners will dive into model deployment strategies:Real-time inference vs. batch inference using Azure Kubernetes Service (AKS) and Azure Functions.Security best practices - Role-Based Access Control (RBAC), compliance, and encryption. Monitoring model drift - Implementing tracking tools to detect performance degradation over time.Automating Machine Learning WorkflowsThis course includes Azure ML Pipelines to automate machine learning processes: Building end-to-end pipelines - Automate data ingestion, model training, and evaluation.Using custom Python scripts in ML pipelines.Monitoring and managing pipeline execution for scalability and efficiency.MLOps & CI/CD for Machine LearningLearners will gain practical knowledge of MLOps and CI/CD for ML models using:Azure DevOps & GitHub Actions for model versioning and retraining automation.CI/CD pipelines for seamless ML model updates.Techniques for model lifecycle management - Deployment, monitoring, and rollback strategies.Exploring Generative AI with Azure MLThis course also introduces Generative AI: Working with Azure OpenAI Services - GPT, DALL·E, and Codex. Fine-tuning AI models for domain-specific applications. Ethical AI considerations - Bias detection, explainability, and responsible AI practices. Overview Section 1: Introduction to Machine Learning and Azure Lecture 1 Definition and Overview of machine learning (ML) Lecture 2 Types of machine learning Supervised, Unsupervised, Reinforcement Learning. Lecture 3 Key concepts Training data, features, labels, models, predictions Lecture 4 Real-world applications of ML in industries such as healthcare, finance, and r Lecture 5 Challenges in machine learning Overfitting, underfitting, data quality, and in Lecture 6 Introduction to Azure ML Studio and its capabilities for building, training, a Lecture 7 Overview of the Azure Machine Learning workspace Datasets, experiments, models Lecture 8 Key components Designer, Notebooks, Automated ML, and Model Management Lecture 9 Key features Visual interface, AutoML, integration with Azure services (Data F Lecture 10 Scalability and flexibility with Azure Compute and storage options Lecture 11 Collaboration and sharing Team-based development and version control Lecture 12 Benefits Faster experimentation, model deployment, and continuous learning Lecture 13 Creating an Azure account Lecture 14 Exploring Azure Cloud Interface and Services Part-1 Lecture 15 Exploring Azure Cloud Interface and Services Part-2 Lecture 16 Exploring Azure Cloud Interface and Services Part-3 Lecture 17 Creating Azure ML Studio Lecture 18 Exploring key features and benefits of Azure ML Studio Lecture 19 Overview of resource management Workspaces, compute resources, and storage acc Lecture 20 Connecting to data sources and Azure services. Section 2: Data Basics and Preprocessing Lecture 21 Importing datasets from various sources local files, Azure Blob Storage, SQL d Lecture 22 Exploring dataset statistics and visualizing data distribution Lecture 23 Understanding data types (numerical, categorical, text, image) Lecture 24 DEMO Loading a dataset and exploring basic statistics in Azure ML Studio Lecture 25 Identifying and handling missing data ( Null, Nan Values ) Lecture 26 Outlier detection and treatment strategies Lecture 27 Removing duplicates and irrelevant issues Lecture 28 Correcting data types and formatting issues Lecture 29 DEMO - Cleaning a dataset by handling missing values and outliers in ML Studio Lecture 30 Exploring ML Studio Designer and Setting up an Experiment If you're new to ML and want a structured, hands-on introduction using Azure Machine Learning Studio, this course will provide step-by-step guidance.,If you have some knowledge of ML but want to scale your models using Azure's cloud-based ML tools, this course will help you learn model training, deployment, and automation.,If you work with data and want to transition into machine learning and AI, this course will teach you how to build, optimize, and deploy ML models efficiently in Azure ML Studio.,you're an Azure user, cloud engineer, or solutions architect, this course will teach you how to integrate Azure ML with cloud-based services for AI-driven solutions.,If you're a software developer or Python programmer looking to automate machine learning workflows and deploy AI solutions, this course will provide the skills you need.,If you're interested in MLOps, CI/CD for ML models, and automated retraining, this course covers end-to-end model lifecycle management in Azure ML.,If you work in healthcare, finance, retail, cybersecurity, or any data-driven industry, this course will show you how machine learning can solve real-world business problems. 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Free Download Udemy - Mathematics For Machine Learning And Llms Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.81 GB | Duration: 15h 28m How is math used in AI What you'll learn Machine Learning mathematics linear algebra, statistics, probability and calculus for machine learning How algorithms works How algorithms are parametrizided Requirements Basic notions of machine learning Description Machine Learning is one of the hottest technologies of our time! If you are new to ML and want to become a Data Scientist, you need to understand the mathematics behind ML algorithms. There is no way around it. It is an intrinsic part of the role of a Data Scientist and any recruiter or experienced professional will attest to that. The enthusiast who is interested in learning more about the magic behind Machine Learning algorithms currently faces a daunting set of prerequisites: Programming, Large Scale Data Analysis, mathematical structures associated with models and knowledge of the application itself. A common complaint of mathematics students around the world is that the topics covered seem to have little relevance to practical problems. But that is not the case with Machine Learning.This course is not designed to make you a Mathematician, but it does provide a practical approach to working with data and focuses on the key mathematical concepts that you will encounter in machine learning studies. It is designed to fill in the gaps for students who have missed these key concepts as part of their formal education, or who need to catch up after a long break from studying mathematics.Upon completing the course, students will be equipped to understand and apply mathematical concepts to analyze and develop machine learning models, including Large Language Models. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 The Learning Diagram Lecture 3 Python Section 2: Types of Learning Lecture 4 Supervised Learnimg Lecture 5 Unsupervised Learning Lecture 6 Reinforcement Learning Lecture 7 When to Use and Not to Use ML Lecture 8 How to chose ML Algorithms Section 3: Data Preparation Lecture 9 Preeliminar Analysis Lecture 10 The Target Variable Lecture 11 Missing Data Lecture 12 Log Transformation - Homocedasticity Lecture 13 Outliers and Anomaly Detection Lecture 14 Data Transformation Lecture 15 Data Transformation (cont.) Section 4: Statistics in the Context off ML Lecture 16 Significant Differences Lecture 17 Descriptive and Inferential Statistics Section 5: Descriptive Statistics Lecture 18 Variables and Metrics Lecture 19 Correlation and Covariance Section 6: Probabilities for ML Lecture 20 Uncertainity Lecture 21 Frquentist versus Bayesian Probabilities Lecture 22 Random Variables and Sampling Lecture 23 Sampling Spaces Lecture 24 Basic Definitions of Probabilities Lecture 25 Axions, Theorems, Independence Lecture 26 Conditional Probability Lecture 27 Bayes Theorem and Naive Bayes Algorithm Lecture 28 Expectation, Chance and Likelihood Lecture 29 Maximum Likelihood Estimation (MLE) Lecture 30 Simulations Lecture 31 Monte Carlo Simulation, Markov Chainn Lecture 32 Probability Distributions Lecture 33 Families of Distributions Lecture 34 Normal Distribution Lecture 35 Tests for Normality Lecture 36 Exponential Distribution Lecture 37 Weibull Distribution and Survival Analysis Lecture 38 Binomial Distribution Lecture 39 Poisson Distribution Section 7: Statiscs Tests Lecture 40 Hypothesis Testing Lecture 41 The p- value Lecture 42 Critical Value, Significance, Confidence, CLT, LLN Lecture 43 Z and T Tests Lecture 44 Degrees of Freedom and F statistics Lecture 45 ANOVA Lecture 46 Chi Squared Test Lecture 47 Statistical Power Lecture 48 Robustness and Statistical Sufficiency Section 8: Time Series Lecture 49 Times Series Decommposition Lecture 50 Autoregressive Models Lecture 51 Arima Section 9: Linear ad Non Linear Models Lecture 52 Linear and Non Linear Models Section 10: Linear Algebra for ML Lecture 53 Introduction to Linear Algebra Lecture 54 Types of Matrices Lecture 55 Matrices Operations Lecture 56 Linear Transformations Lecture 57 Matrix Decomposition and Tensors Section 11: Calculus for ML Lecture 58 Functions Lecture 59 Limits Lecture 60 The Derivative Lecture 61 Calculating the Derivative Lecture 62 Maximum and Minimum Lecture 63 Analitical vs Numerical Solutions Lecture 64 Numerical and Analytic Solution Lecture 65 Gradient Descent Section 12: Distances, Similarities, knn and k means Lecture 66 Distance Measurements Lecture 67 Similarities Lecture 68 Knn and K means Lecture 69 Distances in Python Section 13: Training, Testing ,Validation Lecture 70 Training, Testin, Validation Lecture 71 Training, Testing, Validation (cont) Section 14: The Cost Function Lecture 72 The Cost Function Lecture 73 Cost Function for Regression and Classification Lecture 74 Minimazing the Cost Function with Gradient Descent Lecture 75 Batch annd Stochastic Gradient Descent Section 15: Bias and Variance Lecture 76 Bias and Variance Introduction Lecture 77 Complexity Lecture 78 Regularization Lecture 79 Regularization (Cont) Section 16: Parametric andd Non Parametric Algorithms Lecture 80 Parametric and Non Parametric Algorithms Section 17: Learning Curves Lecture 81 Learning Curves Lecture 82 Learning Curves in Python Section 18: Dimensionality Reduction Lecture 83 PCA and SCD Lecture 84 Eigenvectors and Eigenvalues Lecture 85 Dimensionality Reduction in Python Section 19: Entropy and Information Gain Lecture 86 Entropy and Information Gain Lecture 87 Entropy and Information Gain (cont) Section 20: Linear Regression Lecture 88 Linear Regression Lecture 89 Linear Regression (cont) Lecture 90 Polinomial Regression Section 21: Classification Lecture 91 Logistic Function Lecture 92 Generalized Linear Models (GLM) Lecture 93 Decision Boundaries Lecture 94 Confusion Matrix Lecture 95 ROC and AUC Lecture 96 Visualization of Class Distribution Lecture 97 Precision and Recall Section 22: Decision Trees Lecture 98 Introduction to Decision Trees Lecture 99 Gini Index Lecture 100 Hyperparameters Lecture 101 Decision Trees in Python Section 23: Suport Vector Machines Lecture 102 Introduction to SVMs Lecture 103 Introduction to SVMs (cont) Lecture 104 Mathematics of SVMs Lecture 105 SVM in Python Section 24: Ensemble Algorithms Lecture 106 Wisdom of the Crowds Lecture 107 Bagging and Random Forest Lecture 108 Adaboost, Gradient Boosting, XGBoosting Section 25: Natural Language Processing Lecture 109 Introduction to NLP Lecture 110 Tokenization and Embeddings Lecture 111 Weights and Representation Lecture 112 Sequences and Sentiment Analysis Section 26: Neural Networks Lecture 113 Mathematical Model of Artificial Neuron Lecture 114 Activation Functions Lecture 115 Activation Functions (cont) Lecture 116 Weights and Bias Parameters Lecture 117 Feedforward and Backpropagation Concepts Lecture 118 Feedforward Process Lecture 119 Backpropagation Process Lecture 120 Recurent Neural Networks (RNN) Lecture 121 Convolution Neural Networks (CNN) Lecture 122 Convolution Neural Networks (CNN) (cont) Lecture 123 Seq2Seq and Aplications of NN Section 27: Large Language Models Lecture 124 Generative vs Descriptive AI Lecture 125 LLMs Properties Section 28: Transformers Lecture 126 Introduction to Transformers Lecture 127 Training and Inference Lecture 128 Basic Arquitecture of Transformers Lecture 129 Encoder Workflow Lecture 130 Sel Attention Lecture 131 Multi-Head Attention Lecture 132 Normalization and Residual Connections Lecture 133 Decoder Lecture 134 Types of Transformers Arquitecture Data Scientists and AI professionals Homepage: https://www.udemy.com/course/mathematics-for-machine-learning-and-llms/ DOWNLOAD NOW: Udemy - Mathematics For Machine Learning And Llms Rapidgator https://rg.to/file/83fa111daccde3a58d87ef278952048e/bcmff.Mathematics.For.Machine.Learning.And.Llms.part1.rar.html https://rg.to/file/9a9ecbca8ff6722e7ad89b8d169f7897/bcmff.Mathematics.For.Machine.Learning.And.Llms.part3.rar.html https://rg.to/file/ab651b47d1faa554346a88ca8d4ed50a/bcmff.Mathematics.For.Machine.Learning.And.Llms.part2.rar.html Fikper Free Download https://fikper.com/2EnkhSDlAP/bcmff.Mathematics.For.Machine.Learning.And.Llms.part1.rar.html https://fikper.com/jhjLb2L2M5/bcmff.Mathematics.For.Machine.Learning.And.Llms.part2.rar.html 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Free Download Udemy - Machine Learning Masterclass (2025) Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 8.73 GB | Duration: 26h 3m Combine Theory and Practice and become a Machine Learning Expert. Learn the basics of math and make real applications. What you'll learn Understand the fundamentals of Machine Learning and its real-world applications. Implement ML models using Python, TensorFlow, PyTorch, and Scikit-learn. Preprocess data, perform feature engineering, and optimize models effectively. Build, evaluate, and deploy ML models for classification, regression, and clustering. Requirements No prior knowledge of Machine Learning is required. The course covers everything from the basics. Basic Python programming knowledge is helpful but not mandatory. A Python introduction section is included. A computer with internet access and the ability to install Python-related libraries. Enthusiasm to learn and apply Machine Learning concepts in real-world scenarios. Description Master Machine Learning: A Complete Guide from Fundamentals to Advanced TechniquesMachine Learning (ML) is rapidly transforming industries, making it one of the most in-demand skills in the modern workforce. Whether you are a beginner looking to enter the field or an experienced professional seeking to deepen your understanding, this course offers a structured, in-depth approach to Machine Learning, covering both theoretical concepts and practical implementation.This course is designed to help you master Machine Learning step by step, providing a clear roadmap from fundamental concepts to advanced applications. We start with the basics, covering the foundations of ML, including data preprocessing, mathematical principles, and the core algorithms used in supervised and unsupervised learning. As the course progresses, we dive into more advanced topics, including deep learning, reinforcement learning, and explainable AI.What You Will LearnThe fundamental principles of Machine Learning, including its history, key concepts, and real-world applicationsEssential mathematical foundations, such as vectors, linear algebra, probability theory, optimization, and gradient descentHow to use Python and key libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch for building ML modelsData preprocessing techniques, including handling missing values, feature scaling, and feature engineeringSupervised learning algorithms, such as Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, and Naive BayesUnsupervised learning techniques, including Clustering (K-Means, Hierarchical, DBSCAN) and Dimensionality Reduction (PCA, LDA)How to measure model accuracy using various performance metrics, such as precision, recall, F1-score, ROC-AUC, and log lossTechniques for model selection and hyperparameter tuning, including Grid Search, Random Search, and Cross-ValidationRegularization methods such as Ridge, Lasso, and Elastic Net to prevent overfittingIntroduction to Neural Networks and Deep Learning, including architectures like CNNs, RNNs, LSTMs, GANs, and TransformersAdvanced topics such as Bayesian Inference, Markov Decision Processes, Monte Carlo Methods, and Reinforcement LearningThe principles of Explainable AI (XAI), including SHAP and LIME for model interpretabilityAn Overview of AutoML and MLOps for deploying and managing machine learning models in productionWhy Take This Course?This course stands out by offering a balanced mix of theory and hands-on coding. Many courses either focus too much on theoretical concepts without practical implementation or dive straight into coding without explaining the underlying principles. Here, we ensure that you understand both the "why" and the "how" behind each concept.Beginner-Friendly Yet Comprehensive: No prior ML experience required, but the course covers everything from the basics to advanced conceptsHands-On Approach: Practical coding exercises using real-world datasets to reinforce learningClear, Intuitive Explanations: Every concept is explained step by step with logical reasoningTaught by an Experienced Instructor: Guidance from a professional with expertise in Machine Learning, AI, and OptimizationBy the end of this course, you will have the knowledge and skills to confidently build, evaluate, and optimize machine learning models for various applications.If you are looking for a structured, well-organized course that takes you from the fundamentals to advanced topics, this is the right course for you. Enroll today and take the first step toward mastering Machine Learning. Beginners who want to learn Machine Learning from scratch.,Students, researchers, and professionals looking to build a strong foundation in ML.,Data analysts, engineers, and programmers who want to expand into Machine Learning.,Anyone interested in applying ML techniques to real-world problems using Python. 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