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Street Machine Australia - November 2024 English | 148 pages | True PDF | 93.8 MB Street Machine is the country's biggest selling, most widely read and most respected modified car magazine. Combining great photography with accurate, expert coverage of the Aussie modified car scene and in-depth technical features, Street Machine celebrates Australia's passion for older cars, V8s and the lifestyle that surrounds them. [img=https://ddownload.com/images/promo/banner_240-32.png] https://ddownload.com/gnzzmjxdbh0h https://rapidgator.net/file/21b80350160dae127d39f09c9c53e984/ https://turbobit.net/t7i48eow4n8x.html

epub | 11.81 MB | English| Isbn:9789815179606 | Author: Abhijit Banubakode, Sunita Dhotre, Chhaya S. Gosavi, G. S. Mate, Nuzhat Faiz Shaikh | Year: 2024 Description: https://ddownload.com/xgnv8c5ntatu https://rapidgator.net/file/afa9875ceb1eb970da7d1be402aa74b8/ https://turbobit.net/bk234yl3t6my.html

Free Download Mastering AI From Machine Learning to Automation Published 10/2024 Created by Ndiaga Fall MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 53 Lectures ( 6h 37m ) | Size: 4.5 GB AI, Machine Learning, Reinforcement Learning, Robotics, Automation What you'll learn: Automation Artificial intelligence Machine Learning Field Knowlegde Robotics Reinforcement Learning Requirements: No prior experience in Artificial Intelligence is required Description: Unlock the power of Artificial Intelligence (AI) and gain hands-on experience with cutting-edge technologies like Machine Learning, Reinforcement Learning, Robotics, Automation, and Natural Language Processing (NLP). This course is designed to take you from AI fundamentals to advanced applications, empowering you to build intelligent systems and solve real-world challenges.What You Will Learn:Artificial Intelligence Foundations: Understand the core principles of AI and how it's revolutionizing industries like healthcare, finance, and robotics.Machine Learning (ML): Dive into supervised and unsupervised learning, build predictive models, and master techniques like decision trees, neural networks, and deep learning.Reinforcement Learning (RL): Explore how intelligent agents make decisions through trial and error in dynamic environments, using algorithms that optimize long-term success.Robotics & Automation: Discover how AI-driven robots are transforming automation processes and how machine learning is enabling robots to interact with their surroundings in real time.Natural Language Processing (NLP): Learn to build models that understand, generate, and respond to human language, using techniques such as sentiment analysis, text generation, and speech recognition.Real-World Applications: Implement AI solutions for automation, decision-making, and optimization tasks in various industries, and explore the future potential of AI in robotics and beyond.Why Take This Course? Whether you're an aspiring data scientist, an AI enthusiast, or a professional looking to automate processes, this course offers a complete roadmap to mastering AI. By the end of the course, you will have the skills to build intelligent systems that can learn, adapt, and interact with the world around them.Join us today and step into the future of AI and robotics! Who this course is for: Beginners curious about Artificial Intelligence Homepage https://www.udemy.com/course/mastering-ai-from-machine-learning-to-automation/ Rapidgator https://rg.to/file/460a6233b268963abd3bd4a9fe60e766/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part1.rar.html https://rg.to/file/9463bab2d2169ebe876e3e0945e26942/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part2.rar.html https://rg.to/file/9c1df9307a52b94723c3e2d4d9931b76/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part3.rar.html https://rg.to/file/9f0b52b9071fcccc6f12ad9999a2ff5b/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part4.rar.html https://rg.to/file/af3cdb3c5c4b3a32b6e8a3f4c448eef5/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part5.rar.html Fikper Free Download https://fikper.com/JvlEWN1jIB/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part1.rar https://fikper.com/62BOmMn010/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part2.rar https://fikper.com/4oH3PqPVuv/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part3.rar https://fikper.com/Xtm7teNRik/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part4.rar https://fikper.com/v9QvlBDT49/opikl.Mastering.AI..From.Machine.Learning.to.Automation.part5.rar No Password - Links are Interchangeable

Free Download MQL5 MACHINE LEARNING Linear Regression for Algo Trading Published 10/2024 Created by Latvian Trading Solutions MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 25 Lectures ( 3h 42m ) | Size: 2 GB A complete guide to developing linear regression based models for algorithmic trading in MQL5 What you'll learn: The concept of Linear Regression and its application in Algorithmic Trading How to Develop a Linear Regression model on a spread sheet How to code a Linear Regression model Indicator in MQL5 How to develop a Linear Regression Strategy and code an Expert advisor in MQL5 Requirements: Basics of MQL5 Description: Simple linear regression is a statistical method used to model the relationship between two variables: an independent variable (x) and a dependent variable (y). It assumes a linear relationship between the two variables and aims to find the best-fitting straight line that represents this relationship.The equation for a simple linear regression model is:y = ax + bWhere:y is the dependent variable (the variable we want to predict).x is the independent variable (the variable used to make predictions).a is the slope of the line, representing the rate of change of y with respect to x.b is the y-intercept, representing the value of y when x is zero.While simple linear regression is a statistical technique, it can also be considered as a machine learning algorithm. In machine learning, the goal is to build models that can learn from data and make predictions. Linear regression fits this framework because it learns the relationship between x and y from a given dataset and uses this learned relationship to make predictions for new data points. As neural networks learn the best non-linear relationships between data by finding the weights that best fit the data, linear regression aims to find the best values of a and b that best describe the linear relationship between variables.In this course, our aim is to build a linear regression model in mql5 that seeks to predict the closing prices of a currency pair given its specific bar index. We shall start by creating a linear regression model on a spread sheet to basically explain the calculations involved in creating a linear regression model. We shall then develop our linear regression model as an mql5 indicator by coding it using the mql5 programming language. After that, we shall develop our trading strategy as an mql5 expert advisor coded using the mql5 algorithmic trading language. We shall use the linear regression model we created as an indicator to analyze data and find patterns we can use to profit from the market. We shall base our trading logic on the fact that if price goes beyond one or two standard deviations from its predicted or expected price, it has to reverse and go back to its expected price. Hence our strategy will be a mean reversion type of strategy.For those that are still finding their way with MQL5, as long as you understand the basics of MQL5, this course is for you. We will patiently guide you through every step of the strategy development process and walk you through every line of code we shall craft. Hopefully, by the end of the course, you will have gained the necessary skills to code similar models and trading strategies and be able to appreciate how linear regression models can be an asset in developing your own trading ideas based on the ideas that shared in this course.So hit hard on that enroll button now and join me in this incredible journey of coding a linear regression model using the mql5 algorithmic trading language. Who this course is for: Anyone willing to learn about the applications of Linear Regression in Market analysis and timeseries forecasting Homepage https://www.udemy.com/course/mql5-machine-learning-linear-regression-for-algo-trading/ Rapidgator https://rg.to/file/630d36f1b4288747a471e52674d9d4dd/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar.html https://rg.to/file/7645de1e68ac2dcf4f2b70fa72700e70/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar.html https://rg.to/file/482c3aed89c0666e8b54135282d626d2/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar.html Fikper Free Download https://fikper.com/1HmmIr9mbW/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part1.rar https://fikper.com/R3AmHQhj0Z/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part2.rar https://fikper.com/4wDuaEE6tI/tckwc.MQL5.MACHINE.LEARNING.Linear.Regression.for.Algo.Trading.part3.rar No Password - Links are Interchangeable

Free Download Solana Candy Machine Deployment & Minting Dapp Integration Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 793.08 MB | Duration: 0h 46m Comprehensive Guide to Launching NFTs and Building a Minting DApp on Solana What you'll learn Generate NFTs with image layers and generate metadata files for the Solana blockchain Create Config file for the Candy machine Create a Candy machine with Sugar and upload the NFTs Add Candy guards to the Candy machine and view the details Interact with Candy machine and fetch data Create a Solana NFT minting dapp and perform minting Generate Solana key pair and request airdrops Switch between Devnet and Mainnet Requirements You will need node version 16.16 or higher version to be installed to your environment (I used v16.16) I recommend to use Visual studio code as the editor Sugar v2.7.1 Use Solana CLI latest version A little knowledge in react js is good to have Description This guide provides a comprehensive, step-by-step approach to generating NFTs, deploying a Solana Candy Machine, and integrating it into a minting dApp. It's designed to help developers launch a full NFT project on the Solana blockchain, from asset creation to user-friendly minting.In the first part of the guide, you'll focus on generating NFTs and creating their metadata. You'll learn how to produce unique NFT images and associate them with metadata files, ensuring compatibility with the Solana NFT standard. By the end of this section, you'll have a complete collection of images and metadata ready for blockchain deployment.The second part covers the deployment of the Solana Candy Machine. You'll be guided through the process of uploading your NFT assets to the blockchain, configuring important parameters such as pricing and minting. This section also explains how to secure your Candy Machine by adding Candy Guards.In the final part of the guide, you'll build and integrate a minting dApp. You'll learn to handle user transactions, and provide real-time minting updates within a user-friendly interface. By the end of the guide, you'll have a fully functional minting dApp, allowing users to easily mint NFTs from your collection.This guide equips you with the knowledge to launch a secure, scalable NFT project on Solana, from asset generation to a live minting dApp. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Prerequisites Section 2: Generate Solana NFT images + Metadata Lecture 3 Generate Solana NFT images + Metadata Section 3: Create and Deploy Candy Machine Lecture 4 Create and Deploy Candy Machine Section 4: Create Minting Dapp Lecture 5 Fetch Data from Candy Machine Lecture 6 Code Mint Function Lecture 7 Display Minted NFT Section 5: Switching Devnet to Mainnet Lecture 8 Switching Devnet to Mainnet Lecture 9 Switching Devnet to Mainnet in Phantom Wallet Beginner Solana Developers curios about NFT minting Homepage https://www.udemy.com/course/solana-candy-machine-deployment-minting-dapp-integration/ Rapidgator https://rg.to/file/465a941086caf8a25dd46926750c6476/qwujj.Solana.Candy.Machine.Deployment..Minting.Dapp.Integration.rar.html Fikper Free Download https://fikper.com/j8Lzl3FM7k/qwujj.Solana.Candy.Machine.Deployment..Minting.Dapp.Integration.rar.html No Password - Links are Interchangeable

Free Download Mastering Machine Learning From Basics To Breakthroughs Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 918.11 MB | Duration: 3h 38m Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models What you'll learn Explore the fundamental mathematical concepts of machine learning algorithms Apply linear machine learning models to perform regression and classification Utilize mixture models to group similar data items Develop machine learning models for time-series data prediction Design ensemble learning models using various machine learning algorithms Requirements Foundations of Mathematics and Algorithms Description This Machine Learning course offers a comprehensive introduction to the core concepts, algorithms, and techniques that form the foundation of modern machine learning. Designed to focus on theory rather than hands-on coding, the course covers essential topics such as supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. Learners will explore how these algorithms work and gain a deep understanding of their applications across various domains.The course emphasizes theoretical knowledge, providing a solid grounding in critical concepts such as model evaluation, bias-variance trade-offs, overfitting, underfitting, and regularization. Additionally, it covers essential mathematical foundations like linear algebra, probability, statistics, and optimization techniques, ensuring learners are equipped to grasp the inner workings of machine learning models.Ideal for students, professionals, and enthusiasts with a basic understanding of mathematics and programming, this course is tailored for those looking to develop a strong conceptual understanding of machine learning without engaging in hands-on implementation. It serves as an excellent foundation for future learning and practical applications, enabling learners to assess model performance, interpret results, and understand the theoretical basis of machine learning solutions.By the end of the course, parti[beeep]nts will be well-prepared to dive deeper into machine learning or apply their knowledge in data-driven fields, without requiring programming or software usage. Overview Section 1: Introduction Lecture 1 Introduction to Machine Learning Lecture 2 Types of Machine Learning Lecture 3 Polynomial Curve Fitting Lecture 4 Probability Lecture 5 Total Probability, Bayes Rule and Conditional Independence Lecture 6 Random Variables and Probability Distribution Lecture 7 Expectation, Variance, Covariance and Quantiles Section 2: Linear Models for Regression Lecture 8 Maximum Likelihood Estimation Lecture 9 Least Squares Method Lecture 10 Robust Regression Lecture 11 Ridge Regression Lecture 12 Bayesian Linear Regression Lecture 13 Linear models for classification::Discriminant Functions Lecture 14 Probabilistic Discriminative and Generative Models Lecture 15 Logistic Regression Lecture 16 Bayesian Logistic Regression Lecture 17 Kernel Functions Lecture 18 Kernel Trick Lecture 19 Support Vector Machine Section 3: Mixture Models and EM Lecture 20 K-means clustering Lecture 21 Mixtures of Gaussians Lecture 22 EM for Gaussian Mixture Models Lecture 23 PCA, Choosing the number of latent dimensions Lecture 24 Hierarchial clustering Students, data scientists and engineers seeking to solve data-driven problems through predictive modeling Homepage https://www.udemy.com/course/mastering-machine-learning-from-basics-to-breakthroughs/ Rapidgator https://rg.to/file/7a1c1299ff4b931eddf50e6b453b5dbb/ddbol.Mastering.Machine.Learning.From.Basics.To.Breakthroughs.rar.html Fikper Free Download https://fikper.com/46pB4R63M3/ddbol.Mastering.Machine.Learning.From.Basics.To.Breakthroughs.rar.html No Password - Links are Interchangeable

Free Download Introduction to AI and Machine Learning on Google Cloud Duration: 2h 50m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 378 MB Genre: eLearning | Language: English This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises. Homepage https://www.pluralsight.com/courses/introduction-ai-machine-learning-google-cloud TakeFile https://takefile.link/mt7ylysqlg62/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html Rapidgator https://rg.to/file/3bb6644cc7ee9b81d506bb54bd54f855/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html Fikper Free Download https://fikper.com/L2Wl0zcgJY/clutv.Introduction.to.AI.and.Machine.Learning.on.Google.Cloud.rar.html No Password - Links are Interchangeable

Free Download Math 0-1 Probability for Data Science & Machine Learning Published 9/2024 Created by Lazy Programmer Team,Lazy Programmer Inc. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 94 Lectures ( 17h 30m ) | Size: 7.62 GB A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers What you'll learn: Conditional probability, Independence, and Bayes' Rule Use of Venn diagrams and probability trees to visualize probability problems Discrete random variables and distributions: Bernoulli, categorical, binomial, geometric, Poisson Continuous random variables and distributions: uniform, exponential, normal (Gaussian), Laplace, Gamma, Beta Cumulative distribution functions (CDFs), probability mass functions (PMFs), probability density functions (PDFs) Joint, marginal, and conditional distributions Multivariate distributions, random vectors Functions of random variables, sums of random variables, convolution Expected values, expectation, mean, and variance Skewness, kurtosis, and moments Covariance and correlation, covariance matrix, correlation matrix Moment generating functions (MGF) and characteristic functions Key inequalities like Markov, Chebyshev, Cauchy-Schwartz, Jensen Convergence in probability, convergence in distribution, almost sure convergence Law of large numbers and the Central Limit Theorem (CLT) Applications of probability in machine learning, data science, and reinforcement learning Requirements: College / University-level Calculus (for most parts of the course) College / University-level Linear Algebra (for some parts of the course) Description: Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Prin[beeep]l Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with a deep understanding of probability so that you can apply it correctly and effectively in data science, machine learning, and beyond.Are you ready?Let's go!Suggested prerequisites:Differential calculus, integral calculus, and vector calculusLinear algebraGeneral comfort with university/collegelevel mathematics Who this course is for: Python developers and software developers curious about Data Science Professionals interested in Machine Learning and Data Science but haven't studied college-level math Students interested in ML and AI but find they can't keep up with the math Former STEM students who want to brush up on probability before learning about artificial intelligence Homepage https://www.udemy.com/course/probability-data-science-machine-learning/ Rapidgator https://rg.to/file/32b6b9086c50a019fba6df0e902c5fbc/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part5.rar.html https://rg.to/file/41154f19b9beb47677595b4c02d06ef0/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part4.rar.html https://rg.to/file/7b80052282597412fce1092da6ad6e5b/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part3.rar.html https://rg.to/file/8029dad93bccbf17819b32d783cedeb6/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part8.rar.html https://rg.to/file/afc6d03ec267b0c96440630d43977338/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part7.rar.html https://rg.to/file/b02a8e41004d037ba1af051f7e6ddeec/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part1.rar.html https://rg.to/file/df9764881658d8feef6afd16c2e33f18/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part2.rar.html https://rg.to/file/e33ef1f15da383173e5f374d6c05ddf4/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part6.rar.html Fikper Free Download https://fikper.com/8XlFT98L5F/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part2.rar.html https://fikper.com/AbcgkDYJ3P/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part4.rar.html https://fikper.com/FXMd2RPwEt/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part7.rar.html https://fikper.com/HAFsth9Luc/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part1.rar.html https://fikper.com/MW8cOocvWR/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part5.rar.html https://fikper.com/Pg9pHKbIHN/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part6.rar.html https://fikper.com/Y8W4uDUZb8/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part8.rar.html https://fikper.com/pS1ivjLw3K/nmmkg.Math.01.Probability.for.Data.Science..Machine.Learning.part3.rar.html No Password - Links are Interchangeable

Free Download Hands-On Machine Learning with Python - Real Projects Published 9/2024 Created by TechJedi LLP MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 39 Lectures ( 3h 4m ) | Size: 2.2 GB Master Machine Learning with Python: Build, Train & Deploy Models with Real-World Projects What you'll learn: Implement Machine Learning algorithms in Python using libraries like scikit-learn and TensorFlow. Preprocess and analyze datasets to build predictive models. Evaluate model performance and select the best algorithms for various problems. Develop and deploy real-world machine learning applications from scratch. Requirements: Basic knowledge of Python programming is helpful but not mandatory. No prior experience in Machine Learning required - we'll start from the basics. A computer with Python and essential libraries installed (instructions provided in the course). Curiosity and a willingness to learn - the course is designed for all levels! Description: Dive into the exciting world of Machine Learning with our comprehensive course designed for aspiring data scientists, Python developers, and AI enthusiasts. This course will equip you with the essential skills and practical knowledge to harness the power of Machine Learning using Python.You will begin with the fundamentals of Machine Learning, exploring its definition, types, and workflow, while setting up your Python environment. As you progress, you'll delve into data preprocessing techniques to ensure your datasets are clean and ready for analysis.The course covers supervised and unsupervised learning algorithms, including Linear Regression, Decision Trees, K-Means Clustering, and Prin[beeep]l Component Analysis. Each section features hands-on projects that reinforce your understanding and application of these concepts in Python.You will learn to evaluate and select models using metrics and hyperparameter tuning, ensuring your solutions are both effective and efficient. Our in-depth exploration of Deep Learning with TensorFlow will introduce you to neural networks and advanced architectures like Convolutional Neural Networks (CNN).Additionally, you'll discover the essentials of Natural Language Processing (NLP), mastering text preprocessing and word embeddings to extract insights from textual data. As you approach the course's conclusion, you will gain valuable skills in model deployment, learning how to create web applications using Flask and ensure your models are production-ready.Cap off your learning journey with a real-world capstone project where you will apply everything you've learned in an end-to-end Machine Learning workflow, culminating in a presentation and peer review.Whether you are a beginner eager to enter the field or a professional looking to enhance your skill set, this course provides the tools and knowledge necessary to succeed in the dynamic landscape of Machine Learning. Join us and take the first step toward mastering Machine Learning in Python today! Who this course is for: Beginners interested in Machine Learning who want to learn through hands-on projects. Python developers looking to expand their skills in data science and machine learning. Data analysts and statisticians eager to apply machine learning techniques to real-world problems. Anyone curious about AI and Machine Learning who wants to build practical models without prior experience. Homepage https://www.udemy.com/course/hands-on-machine-learning-with-python-real-projects/ Rapidgator https://rg.to/file/02d6ffc63e6ece2a4e12c2e90128cedb/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part3.rar.html https://rg.to/file/338df430412602fc1fc32a7258664316/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part2.rar.html https://rg.to/file/42436adc219bc9af71d070f5eeaf0cdb/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part1.rar.html Fikper Free Download https://fikper.com/KcdzKXVqlA/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part2.rar.html https://fikper.com/uVJ7SQr1t6/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part3.rar.html https://fikper.com/xoPGTfFCF2/ybabz.HandsOn.Machine.Learning.with.Python.Real.Projects.part1.rar.html No Password - Links are Interchangeable

Free Download Google Cloud Big Data and Machine Learning Fundamentals (2024) Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 2h 26m | Size: 240 MB This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud. Homepage https://www.pluralsight.com/courses/google-cloud-big-data-machine-learning-fundamentals-6 TakeFile https://takefile.link/hs3nph95rx2w/bwhhh.Google.Cloud.Big.Data.and.Machine.Learning.Fundamentals.2024.rar.html Rapidgator https://rg.to/file/ea07dc797a799317d467b815cea3a593/bwhhh.Google.Cloud.Big.Data.and.Machine.Learning.Fundamentals.2024.rar.html Fikper Free Download https://fikper.com/QJm9pNQSiW/bwhhh.Google.Cloud.Big.Data.and.Machine.Learning.Fundamentals.2024.rar.html No Password - Links are Interchangeable

Free Download Master Program In Machine Learning & Artificial Intelligence Published 9/2024 Created by IIBM Institute of Business Management MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 20 Lectures ( 28h 13m ) | Size: 16.2 GB Advanced study in ML/AI: algorithms, deep learning, NLP, ethics, big data, hands-on projects, and research opportunities What you'll learn: Foundations of AI & ML Machine Learning Algorithms Data Analytics Business Analysis Data Science with R SQL Database Data Science with Python Deep Learning Natural Language Processing Data Visualization Using Tableau Big Data Requirements: Fresh Graduates/ Diploma in any discipline or equivalent with minimum 1 Year of working Professionals Description: The Master's program in Machine Learning and Artificial Intelligence offers an extensive curriculum designed to provide students with both theoretical knowledge and practical skills in these rapidly evolving fields. The program begins with foundational coursework in mathematics and programming, covering essential topics such as linear algebra, calculus, probability, and advanced programming techniques. Core courses delve into machine learning algorithms, including supervised and unsupervised learning methods, neural networks, and deep learning techniques.Students explore natural language processing (NLP) to understand and develop systems for text and speech analysis, and computer vision for interpreting visual data. The program also addresses the ethical implications of AI, focusing on issues like fairness, bias, and societal impact.Hands-on experience is a key component, with opportunities for practical projects, internships, and a research thesis or capstone project that allows students to apply their knowledge to real-world challenges. With a focus on both theoretical understanding and practical application, graduates are prepared for careers in data science, AI research, and machine learning engineering. The program typically spans 1 to 2 years and offers online study options, with some programs available online from IIBM Institute.Advanced study in ML/AI: algorithms, deep learning, NLP, ethics, big data, hands-on projects, and research opportunities. 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Free Download Advanced Machine Learning & Deep Learning Masterclass 2024 Published 9/2024 Created by Academy of Computing & Artificial Intelligence,Maninda Wickrema Edirisooriya MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 71 Lectures ( 25h 23m ) | Size: 9.36 GB Master AI with Advanced Machine Learning & Deep Learning Techniques: From Neural Networks to Transformers and Beyond What you'll learn: Machine Learning Deep Learning Preprocessing Data Science journey Requirements: Basic Mathematics & Python knowledge Description: Welcome to the Advanced Machine Learning & Deep Learning Masterclass 2024! This comprehensive course is designed for both business professionals and researchers, offering over 24 hours of in-depth video content. 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Free Download AWS Certified Machine Learning Engineer - Associate MLA-C01 Published 9/2024 Created by Nikolai Schuler MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 250 Lectures ( 25h 50m ) | Size: 10.7 GB The ONLY course you need to PASS the AWS Certified Machine Learning Engineer Exam | MLA-C01 | Incl. FULL Practice Exam! What you'll learn: PASS the AWS Certified Machine Learning Engineer Associate Exam (MLA-C01) Full Practice Exam incl. Full Explanations to ACE the exam All Slides available as downloadable PDFs All Topics Covered & 100% up-to-date Hands-on Demos with Real-World Scenarios Start your Machine Learning Career Build, Train & Deploy Machine Learning Models in Amazon SageMaker Data Ingestion and Preprocessing with SageMaker Data Wrangler Full Machine Learning Pipelines with SageMaker & Much More Master the Full Machine Learning Lifecycle with Real-World Skills Requirements: No previous experience with AWS or Machine Learning is needed! 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Free Download Simplified Machine Learning End To End™ Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.01 GB | Duration: 7h 15m With Case Study This comprehensive course offers an in-depth journey into Machine Learning and Data Science What you'll learn Introduction to Machine Learning:- Understand the basics and types of Machine Learning. ML Unsupervised Learning:- Learn the concepts and techniques of Unsupervised Learning. Supervised Learning - Regression:- Master regression models for predicting continuous outcomes. Evaluation Metrics for Regression Model:- Evaluate regression models using metrics like MSE, RMSE, and R-squared. Supervised Learning - Classification in Machine Learning:- Learn classification algorithms for categorical predictions. Supervised Learning - Decision Trees:- Understand how Decision Trees work for classification and regression. Unsupervised Learning - Clustering:- Explore clustering techniques to group data points. Unsupervised Learning - DBSCAN Clustering: Apply the DBSCAN algorithm for density-based clustering. Unsupervised Learning - Dimensionality Reduction:- Learn techniques to reduce data dimensions while retaining key information. Unsupervised Learning - Dimensionality Reduction with t-SNE:- Use t-SNE for visualizing high-dimensional data in a reduced form. Model Evaluation and Validation Techniques:- Understand model validation methods like cross-validation. Model Evaluation - Bias-Variance Tradeoffs:- Learn to balance bias and variance for improved model performance. Introduction to Python Libraries for Data Science:- Get familiar with key Python libraries such as NumPy, Pandas, and Scikit-learn. Introduction to Python Libraries for Data Science:- Explore advanced Python libraries used in data analysis and machine learning. Introduction to R Libraries for Data Science:- Learn essential R libraries for data manipulation and modeling. Introduction to R Libraries for Data Science Statistical Modeling:- Apply statistical modeling using R's powerful libraries. Requirements Basic Understanding of Mathematics Familiarity with linear algebra, probability, and statistics is helpful. Basic Analytical and Problem-Solving Skills Ability to think critically and solve complex problems. Anyone can learn this class it is very simple. Description This comprehensive course offers an in-depth journey into Machine Learning and Data Science, designed to equip students with the skills needed to build and evaluate models, interpret data, and solve real-world problems. 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Free Download Coursera - Machine Learning Theory and Hands-on Practice with Python Specialization Last updated 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 71 Lessons ( 14h 34m ) | Size: 1.4 GB Develop Foundational Machine Learning Skills. Add Supervised, Unsupervised, and Deep Learning techniques to your Data Science toolkit. What you'll learn Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics. Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm's strengths and weaknesses. Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data's properties. Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization. Skills you'll gain Unsupervised Learning Python Programming Deep Learning hyperparameter tuning Supervised Learning In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs. This specialization can be taken for academic credit as part of CU Boulder's MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science:https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science:https://coursera.org/degrees/ms-computer-science-boulder Applied Learning Project In this specialization, you will build a movie recommendation system, identify cancer types based on RNA sequences, utilize CNNs for digital pathology, practice NLP techniques on disaster tweets, and even generate your images of dogs with GANs. You will complete a final supervised, unsupervised, and deep learning project to demonstrate course mastery. Homepage https://www.coursera.org/specializations/machine-learnin-theory-and-hands-on-practice-with-pythong-cu TakeFile https://takefile.link/slfcret4wdxa/qwqya.Coursera..Machine.Learning.Theory.and.Handson.Practice.with.Python.Specialization.part1.rar.html https://takefile.link/couswvh71783/qwqya.Coursera..Machine.Learning.Theory.and.Handson.Practice.with.Python.Specialization.part2.rar.html Rapidgator http://peeplink.in/907d46c302f7 Fikper Free Download https://fikper.com/zxz1kOVrwX/qwqya.Coursera..Machine.Learning.Theory.and.Handson.Practice.with.Python.Specialization.part1.rar.html https://fikper.com/2k2TCX4W3K/qwqya.Coursera..Machine.Learning.Theory.and.Handson.Practice.with.Python.Specialization.part2.rar.html No Password - Links are Interchangeable

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Whether you're a seasoned Android developer or new to the scene, this course has something valuable to offer youCourse Overview: We'll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Android.The Android-ML Fusion: After grasping the core concepts, we'll bridge the gap between Android and Machine Learning. To do this, we'll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model trainingUnlocking Data's Power: To prepare and analyze our datasets effectively, we'll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data's potential for accurate predictions.Tensorflow for Mobile: Next, we'll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including AndroidRegression Models TrainingTraining Your First Machine Learning Model:Harness TensorFlow and Python to create a simple linear regression modelConvert the model into TFLite format, making it compatible with AndroidLearn to integrate the tflite model into Android apps for AndroidFuel Efficiency Prediction:Apply your knowledge to a real-world problem by predicting automobile fuel efficiencySeamlessly integrate the model into a Android app for an intuitive fuel efficiency prediction experienceHouse Price Prediction in Android:Master the art of training machine learning models on substantial datasetsUtilize the trained model within your Android app to predict house prices confidentlyComputer Vision Model TrainingImage Classification in Android:Collect and process dataset for model trainingTrain image classification models on custom datasets with Teachable MachineTrain image classification models on custom datasets with Transfer LearningUse image classification models in Android with both images and live camera footageObject Detection in AndroidCollect and Annotate Dataset for Object Detection Model TrainingTrain Object Detection ModelsUse object detection models in Android with Images & VideosThe Android Advantage: By the end of this course, you'll be equipped to:Train advanced machine learning models for accurate predictionsSeamlessly integrate tflite models into your Android applicationsAnalyze and use existing regression & vision (ML) models effectively within the Android ecosystemWho Should Enroll:Aspiring Android developers eager to add predictive modeling to their skillsetBeginner Android developer with very little knowledge of mobile app development Intermediate Android developer wanted to build a powerful Machine Learning-based applicationExperienced Android developers wanted to use Machine Learning models inside their applications.Step into the World of Android and Machine Learning: Join us on this exciting journey and unlock the potential of Android and Machine Learning. 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Free Download Crash Course Introduction To Machine Learning Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 292.05 MB | Duration: 0h 40m Kickstart Your Machine Learning Journey: Hands-On Projects with Python Libraries What you'll learn Learn the key concepts of Machine Learning Get experienced with Jupyter Notebooks Learn how to use Python libraries, such as Scikit-learn, numpy, pandas, matplotlib Data handling & cleaning to be used in Machine Learning Introduced to common ML algorithms Learn to evaluate the performance of a model Have hands-on experience with ML algorithms Requirements Basic understanding of high school mathematics Some Python experience would be helpful Description Welcome to "Crash Course Introduction to Machine Learning"! This course is designed to provide you with a solid foundation in machine learning, leveraging the powerful Scikit-learn library in Python.What You'll Learn:The Basics of Machine Learning: Understand the key concepts and types of machine learning, including supervised, unsupervised, and reinforcement learning.Setting Up Your Environment: Get hands-on experience setting up Python, Jupyter Notebooks, and essential libraries like numpy, pandas, matplotlib, and Scikit-learn.Data Preprocessing: Learn how to load, clean, and preprocess data, handle missing values, and split data for training and testing.Building Machine Learning Models: Explore common algorithms such as Linear Regression, Decision Trees, and K-Nearest Neighbors. Train and evaluate models(Linear Regression), and understand performance metrics like accuracy, R^2 and scatter values in plots to measure the prediction.Model Deployment: Gain practical knowledge on saving your pre-trained model for others to use.This course is structured to provide you with both theoretical understanding and practical skills. Each section builds on the previous one, ensuring you develop a comprehensive understanding of machine learning concepts and techniques.Why This Course?Machine learning is transforming industries and driving innovation. This course equips you with the knowledge and skills to harness the power of machine learning, whether you're looking to advance your career, work on personal projects, or simply explore this exciting field.Prerequisites:Basic understanding of Python programming.No prior knowledge of machine learning is required.Enroll Today!Join me on this journey to become proficient in machine learning with Scikit-learn. By the end of this course, you'll have the confidence to build, evaluate, and deploy your machine learning models. Let's get started! Overview Section 1: Introduction Lecture 1 Introduction Section 2: Basics of Machine Learning Lecture 2 AI vs Machine Learning vs Deep Learning Lecture 3 Types of Machine Learning Lecture 4 Key Terminology Section 3: Setting up the environment Lecture 5 Installing Anaconda Distribution Lecture 6 The importance of Jupyter Notebooks Section 4: Data Preprocessing Lecture 7 Data Loading & Cleaning Lecture 8 Data Splitting Section 5: Building a simple ML model Lecture 9 Introduction to ML models & using one Lecture 10 Common ML models Lecture 11 Evaluating accuracy Section 6: Saving the trained model Lecture 12 Saving the model using Pickle Lecture 13 Publishing the ML model Section 7: Conclusion and Next Steps Lecture 14 Recap of What You've Learned Lecture 15 Resources Section 8:[Extra] Improving a model's performance Lecture 16 5 common methods to improve a model's performance Anyone eager enough to learn how machine learning works and to break down the magic to reality Homepage https://www.udemy.com/course/crash-course-machine-learning/ Rapidgator https://rg.to/file/b83e7ace8f870d389cc904a686954d11/ojnlv.Crash.Course.Introduction.To.Machine.Learning.rar.html Fikper Free Download https://fikper.com/8bVS7RovTe/ojnlv.Crash.Course.Introduction.To.Machine.Learning.rar.html No Password - Links are Interchangeable

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