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Free Download Udemy - Python Masterclass by Emir Balic (2025) Published: 3/2025 Created by: Emir Balic MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 194 Lectures ( 21h 4m ) | Size: 10.8 GB Master programming with Python and improve your coding skills from any level to professional What you'll learn Master Python Fundamentals and Syntax Apply Object-Oriented Programming (OOP) Techniques Develop Debugging and Error Handling Skills Understand and Implement Functions and Functional Programming Concepts Utilize Data Structures Effectively (Lists, Dictionaries, Tuples, Sets) Create and Manage Python Modules and Packages Handle File I/O and Work with CSV/JSON Data Implement Best Practices for Code Readability and Maintainability Work with External Libraries Using pip and Virtual Environments Understand and Use Python's Built-in and Custom Exceptions Utilize Regular Expressions for Pattern Matching and Text Processing Optimize Code Performance with Recursion and Functional Techniques Leverage Python for Data Manipulation and Analysis using Pandas Create Real-World Projects to Build a Strong Python Portfolio Requirements Computer with any standard OS (MacOs, Windows, Linux). Desire to become Python developer. Description This course is the best possible mix of theory and hands-on practice to guide you how to become a real Python programmer.Whether you are a complete beginner, switching from another language, or looking to deepen your skills, this course has you covered. With tons of quizzes, practical sessions, and challenging assignments, every concept is reinforced through real coding. You will not just learn Python - you will learn to apply it like a pro. This is where the real difference happens.The sessions and assignments are based on real-world systems, and that way you will gain experience that actually matters. Throughout the course, you'll internalize programming principles, clean code practices, and proper code architecture - everything you need to write professional, maintainable code.Some of the points that make this course different from any other course on this platform are:- The Widest Toolset - You will work with a broad range of industry-standard tools, learning how Python fits into different development workflows.- Multi-sensory Learning Approach - This course is build based upon various well-tested advanced techniques (concept mapping, coloring, shaping, progression steps) proven to boost memory and make complex concepts stick.This is more than just another Python course. It's a structured, constantly improving learning experience designed to take you from beginner to pro. 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Free Download Udemy - PSS Sincal Automation With Python Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 4.84 GB | Duration: 7h 38m Automate Power System Modeling, Calculations, and Data Processing with Python & PSS®SINCAL What you'll learn Understand PSS®SINCAL Architecture Automate Data Validation in PSS®SINCAL Automate Network Element Insertion, Updates, and Parameter Variations Implement Automated Simulation Workflows Exchange Data and Results Between PSS®SINCAL and Other Systems Integrate Python with PSS®SINCAL's COM-Based API Build Real-World Use Cases for PSS®SINCAL Automation Requirements Basic Understanding of Power Systems Fundamentals of PSS®SINCAL Python Programming Basics Familiarity with Automation Concepts PSS®SINCAL Software Python (3.x) IDE or Code Editor Access to a Network Model Description In the rapidly evolving world of power system analysis, automation is becoming essential for efficiency and accuracy. This course, "Mastering PSS®SINCAL Automation with Python," is designed to help power system engineers, researchers, and automation enthusiasts leverage Python to automate repetitive tasks in PSS®SINCAL.PSS®SINCAL is a powerful tool used for power flow analysis, short circuit studies, protection coordination, dynamic analysis, and more. However, manual processes can be time-consuming and prone to errors. By integrating Python scripting, you can automate data validation, network element modifications, simulation execution, and result extraction, significantly improving your workflow.What you will learn:Understand PSS®SINCAL's Architecture - Learn how its APIs and database interfaces work.Automate Power System Modeling - Insert, update, and modify network elements programmatically.Perform Automated Calculations - Use Python to execute power flow, short circuit, and dynamic simulations.Implement Parameter Variation Studies - Run sensitivity analyses without manual intervention.Interface with External Systems - Automate data exchange and result processing.Optimize Workflow Efficiency - Reduce errors, save time, and improve productivity.This course is perfect for power system engineers, consultants, grid operators, and researchers who want to enhance their capabilities with automation. Whether you're new to Python or experienced with PSS®SINCAL, this course will guide you through step-by-step implementations with real-world examples.By the end of this course, you'll have the skills to automate complex simulations, integrate Python with PSS®SINCAL, and streamline your power system analysis like a pro! Overview Section 1: Introduction Lecture 1 Introduction Section 2: Setting up Python Environment Lecture 2 Installing Python & Python Interpreter for PSS Sincal step by step Section 3: Performing Steady State Analysis with Python (LF-SC) Lecture 3 Connect to PSS Sincal and Perform Load Flow with Python Lecture 4 Perform Power Flow and Short Circuit (3ph, 2ph) with Python Section 4: Activate and run Python Scripts inside PSS Sincal & Extra Functions Lecture 5 Python Script inside PSS Sincal & User Experience Section 5: CASE 1 - Static analysis, element selection and GUI applications Lecture 6 Automated Load flow and Short Circuit Lecture 7 Apply evaluation filter, creating heatmaps, view and clear results Lecture 8 Select elements by type, apply color highlighting and clean model Section 6: CASE 2 - Simulation Engine, Load Flow, Short Circuit and Database Results Lecture 9 PSS Sincal Engine - Database - Load Flow and Results for Elements Lecture 10 PSS Sincal Engine - Database - Short Circuit - Import - Export - TabView Section 7: CASE 3 - Combine Engine with Power Flow and Short Circuit inside loop Lecture 11 PSS Sincal Engine - Combine Methods inside Loop and view Results GUI SINCAL Section 8: Access element and change parameters via Python in PSS Sincal Lecture 12 Automation PSS Sincal - Access Element and Change Parameter Section 9: Create electrical elements in virtual database and perform calculations Lecture 13 Create Node, Line, Load in Virtual Database and perform Calculations Section 10: Create graphical electrical elements and place them in PSS Sincal GUI Lecture 14 Create Node, Line, Load and place in PSS SINCAL GUI Model Power System Engineers & Consultants,Energy & Grid Operators,Researchers & Academics,PSS®SINCAL Users,Python Enthusiasts in Power Systems Homepage: ?https://www.udemy.com/course/pss-sincal-automation-with-python/ [b]AusFile[/b] https://ausfile.com/vz0dilps0ucq/vzdqn.Pss.Sincal.Automation.With.Python.part1.rar.html https://ausfile.com/tepxm257b5y7/vzdqn.Pss.Sincal.Automation.With.Python.part2.rar.html https://ausfile.com/3vyj21isb0oc/vzdqn.Pss.Sincal.Automation.With.Python.part3.rar.html https://ausfile.com/yis7eofbmjae/vzdqn.Pss.Sincal.Automation.With.Python.part4.rar.html https://ausfile.com/iqvdy9bc866a/vzdqn.Pss.Sincal.Automation.With.Python.part5.rar.html Rapidgator https://rg.to/file/fc38b209f3d0460fb91d319931a34bcd/vzdqn.Pss.Sincal.Automation.With.Python.part1.rar.html https://rg.to/file/363f94bd8cefd6750a20fe6e1432190e/vzdqn.Pss.Sincal.Automation.With.Python.part2.rar.html https://rg.to/file/138b945b6e35ccf1cfab0399e7e79795/vzdqn.Pss.Sincal.Automation.With.Python.part3.rar.html https://rg.to/file/2b7338475d71ea7fa06e278874d1bb25/vzdqn.Pss.Sincal.Automation.With.Python.part4.rar.html https://rg.to/file/373c2d347ed30e445b256bdd9db48c0b/vzdqn.Pss.Sincal.Automation.With.Python.part5.rar.html Fikper Free Download https://fikper.com/VxJWoy8LSC/vzdqn.Pss.Sincal.Automation.With.Python.part1.rar.html https://fikper.com/7L7yGnoUah/vzdqn.Pss.Sincal.Automation.With.Python.part2.rar.html https://fikper.com/WQ6ozGVA9P/vzdqn.Pss.Sincal.Automation.With.Python.part3.rar.html https://fikper.com/ryLjN4OuEz/vzdqn.Pss.Sincal.Automation.With.Python.part4.rar.html https://fikper.com/LJ55OZgve2/vzdqn.Pss.Sincal.Automation.With.Python.part5.rar.html No Password - Links are Interchangeable

Free Download Udemy - Python Coding With Artificial Intelligence Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.94 GB | Duration: 2h 20m Master the Art of Vibe Coding, the AI-Powered Programming Through Storytelling and Role Play What you'll learn Learn how to code in Python with Artificial Intelligence Learn the basics of Vibe Coding with AI Learn how to create Python scripts without writing a single line of code Understanding the methodology to craft Vibe prompts to lead AI in Code writing Requirements No previous skills required, just passion about AI and software development Description Dive into the immersive world of 'Python Vibe Coding' where learning becomes an adventure. In this unique course, you'll become the protagonist of a retro-futuristic techno-thriller set against the mysterious disappearance of a nuclear research facility in 1962. As you progress through the storyline, you'll master the art of crafting effective Vibe Prompts in Python to solve puzzles, unlock new chapters, and ultimately reveal the truth behind the vanished facility. Each module blends narrative storytelling with practical coding challenges, teaching you how to communicate with AI in natural language to generate functional Python code. You'll learn to think like a programmer while speaking like a storyteller, developing skills in prompt engineering that translate directly to real-world programming scenarios. This isn't just a coding course-it's an interactive experience where your prompting skills determine the outcome of the mystery. By the end, you'll have built a portfolio of Python applications while uncovering the secrets of the past through the power of your words. By the end of this course, you will gain the key skills to craft simple Vibe prompts by yourself to generate end-to-end programs on your computer, living a unique experience of storytelling and learning through practical examples the best way to craft effective Vibe prompts to lead the artificial intelligence in the realm of Python code writing. Everyone interested in using Artificial Intelligence for software development in Python Homepage: https://www.udemy.com/course/vibe-coding-in-python-for-beginners/ [b]AusFile[/b] https://ausfile.com/oteksh7k2025/kzgnl.Python.Coding.With.Artificial.Intelligence.part1.rar.html https://ausfile.com/02dlfdglrg59/kzgnl.Python.Coding.With.Artificial.Intelligence.part2.rar.html https://ausfile.com/7l7xbr3bl2sm/kzgnl.Python.Coding.With.Artificial.Intelligence.part3.rar.html https://ausfile.com/xi09w3h0jlii/kzgnl.Python.Coding.With.Artificial.Intelligence.part4.rar.html https://ausfile.com/lh486k33693v/kzgnl.Python.Coding.With.Artificial.Intelligence.part5.rar.html Rapidgator https://rg.to/file/cc75f18255158b885aa63651d7ffb2db/kzgnl.Python.Coding.With.Artificial.Intelligence.part1.rar.html https://rg.to/file/a2edc423406b534ba91c5d6060c8daf2/kzgnl.Python.Coding.With.Artificial.Intelligence.part2.rar.html https://rg.to/file/1a96f6f72a9273cf542feb65b16cc935/kzgnl.Python.Coding.With.Artificial.Intelligence.part3.rar.html https://rg.to/file/e070220cc46e0cb4ec6a7ffcd24050c9/kzgnl.Python.Coding.With.Artificial.Intelligence.part4.rar.html https://rg.to/file/c021e56a19454cb162caccd1e5588417/kzgnl.Python.Coding.With.Artificial.Intelligence.part5.rar.html Fikper Free Download https://fikper.com/bcqrIaIyl1/kzgnl.Python.Coding.With.Artificial.Intelligence.part1.rar.html https://fikper.com/IFxLgQ8ZjD/kzgnl.Python.Coding.With.Artificial.Intelligence.part2.rar.html https://fikper.com/hkcU2EbspJ/kzgnl.Python.Coding.With.Artificial.Intelligence.part3.rar.html https://fikper.com/7Y3PqBjHA4/kzgnl.Python.Coding.With.Artificial.Intelligence.part4.rar.html https://fikper.com/qv0UAhJaDs/kzgnl.Python.Coding.With.Artificial.Intelligence.part5.rar.html No Password - Links are Interchangeable

Free Download Udemy - Master Machine Learning & AI with Python Published: 3/2025 Created by: Paul Carlo Tordecilla MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 4m ) | Size: 2.46 GB Building Intelligent Systems from the Ground Up What you'll learn Understand the theory behind machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Learn data preprocessing, feature engineering, and visualization methods to prepare data for modeling. Gain hands-on experience building and evaluating models for regression, classification, clustering, and recommendation systems using Python. 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. Designed for beginners and intermediate learners alike, this course bridges theory and practice, enabling you to master key concepts, techniques, and tools that drive today's intelligent systems. Whether you're aiming to launch a career in data science, build innovative projects, or simply expand your technical prowess, this course provides the robust foundation and hands-on experience you need.What you'll learnIntroduction to Machine LearningWhat is Machine Learning?Understand the definition, historical evolution, and transformative impact of machine learning in various industries.Types of Machine Learning:Dive deep into supervised, unsupervised, and reinforcement learning with real-world applications.Applications & Tools:Explore practical use cases across industries and get acquainted with the Python ecosystem and essential libraries like NumPy, Pandas, and Scikit-Learn.Data PreprocessingUnderstanding Data:Learn to distinguish between structured and unstructured data, and use visualization techniques to explore datasets.Data Cleaning & Feature Engineering:Master techniques for handling missing data, encoding categorical variables, feature scaling, and engineering new features.Data Splitting:Get hands-on experience with training/testing splits and cross-validation to ensure robust model performance.Regression TechniquesStart with Simple Linear Regression and progress to Multiple Linear, Polynomial Regression, and more advanced methods like Support Vector Regression, Decision Tree, and Random Forest Regression.Learn how to tackle issues like multicollinearity, overfitting, and implement these models using Python.Classification TechniquesFoundational Algorithms:Gain insights into Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) for both binary and multiclass problems.Advanced Methods:Understand Naive Bayes, Decision Trees, and ensemble methods such as Random Forests and boosting algorithms like AdaBoost, GBM, and XGBoost.Deep Dive into XGBoost:Learn the introduction to XGBoost and explore its advanced concepts, making it a powerful tool for your classification tasks.Clustering TechniquesExplore unsupervised learning with K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.Understand how to determine optimal cluster numbers and interpret dendrograms for meaningful insights.Association Rule LearningApriori & Eclat Algorithms:Learn how to mine frequent itemsets and derive association rules to uncover hidden patterns in data.Natural Language Processing (NLP)Text Processing Fundamentals:Delve into tokenization, stopword removal, stemming, and lemmatization.Vectorization Techniques:Build models using Bag of Words and TF-IDF, and explore sentiment analysis to interpret textual data.Deep LearningNeural Networks & Training:Understand the architecture, training processes (forward and backpropagation), and optimization techniques of neural networks.Specialized Networks:Learn about Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) including LSTM for sequence modeling.Hands-On with Keras & TensorFlow:Build, evaluate, and tune models using industry-standard frameworks.Why Enroll?Comprehensive Curriculum:Our course is meticulously structured to take you from foundational concepts to advanced machine learning techniques, ensuring a holistic understanding of the field.Hands-On Learning:With practical labs and real-world projects, you'll not only learn the theory but also gain the experience needed to implement your ideas effectively.Expert Guidance:Learn from seasoned professionals who bring real industry experience and cutting-edge insights into every lesson.Career Advancement:Gain in-demand skills that are highly valued in tech, finance, healthcare, and beyond, positioning you for success in a rapidly evolving job market.Community & Support:Join a vibrant community of learners and experts, engage in discussions, receive feedback, and collaborate on projects to accelerate your learning journey.Enroll Now!Don't miss this opportunity to transform your career with advanced skills in Machine Learning and AI. 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Free Download Coursera - AI Agents and Agentic AI in Python - Powered by Generative AI Specialization Released 3/2025 By Dr. Jules White , Vanderbilt University MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 67 Lessons ( 9h 25m ) | Size: 4.1 GB Learn AI Agent Fundamentals from the Ground Up. Learn how to build resilient AI agents and develop Agentic AI skills in Python that will remain relevant despite the rapidly evolving AI landscape. What you'll learn Build Autonomous AI Agents - Learn how to create AI agents in Python that can operate independently, interact with APIs, and handle real-world tasks. Master Core and Advanced Agent Concepts - Understand agent loops, tool integration, multi-agent collaboration, and design principles. Optimize AI Agents for Real-World Use - Develop resilient, efficient, and maintainable agents while balancing token costs, speed, and predictability. Skills you'll gain Prompt Engineering Agentic AI Architecture Agentic AI AI Agent Architecture Python AI Agents AI Agents Generative AI AI Agents in Python AI Automation ChatGPT chain of thought prompting prompt patterns Large Language Models AI agents are transforming the way we interact with technology, and in this specialization, you'll learn how to build them from the ground up-without the need for complex or rigid frameworks that will be irrelevant next week. Whether you're a beginner or an experienced developer, you'll master the core concepts that power AI agents, giving you full control over their design and capabilities. You'll start with the fundamentals, understanding how agent loops work, then progress to equipping your agents with tools, integrating multi-agent systems, and implementing self-prompting for more autonomous behavior. 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Free Download Master Vibe Coding in Python for beginners with Storytelling Published: 3/2025 Created by: Giorgio Ferrari MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 24 Lectures ( 2h 20m ) | Size: 2.34 GB Master the Art of Vibe Coding, the AI-Powered Programming Through Storytelling and Role Play What you'll learn Learn how to code in Python with Artificial Intelligence Learn the basics of Vibe Coding with AI Learn how to create Python scripts without writing a single line of code Understanding the methodology to craft Vibe prompts to lead AI in Code writing Requirements No previous skills required, just passion about AI and software development Description Dive into the immersive world of 'Python Vibe Coding' where learning becomes an adventure. In this unique course, you'll become the protagonist of a retro-futuristic techno-thriller set against the mysterious disappearance of a nuclear research facility in 1962. As you progress through the storyline, you'll master the art of crafting effective Vibe Prompts in Python to solve puzzles, unlock new chapters, and ultimately reveal the truth behind the vanished facility. Each module blends narrative storytelling with practical coding challenges, teaching you how to communicate with AI in natural language to generate functional Python code. You'll learn to think like a programmer while speaking like a storyteller, developing skills in prompt engineering that translate directly to real-world programming scenarios. This isn't just a coding course-it's an interactive experience where your prompting skills determine the outcome of the mystery. By the end, you'll have built a portfolio of Python applications while uncovering the secrets of the past through the power of your words. By the end of this course, you will gain the key skills to craft simple Vibe prompts by yourself to generate end-to-end programs on your computer, living a unique experience of storytelling and learning through practical examples the best way to craft effective Vibe prompts to lead the artificial intelligence in the realm of Python code writing. Who this course is for Everyone interested in using Artificial Intelligence for software development in Python Homepage: https://www.udemy.com/course/vibe-coding-in-python-for-beginners/ Rapidgator https://rg.to/file/b992e805587169980f2c37631fbe178b/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part1.rar.html https://rg.to/file/5b9ccde310e6a9c1ca0017bd1faa7578/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part2.rar.html https://rg.to/file/6b9e27abcd6408063b38c3d36d52ea47/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part3.rar.html Fikper Free Download https://fikper.com/m8hPr12Hyh/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part1.rar.html https://fikper.com/XK92yfUIMh/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part2.rar.html https://fikper.com/XdNHM0IKsB/jxfpj.Master.Vibe.Coding.in.Python.for.beginners.with.Storytelling.part3.rar.html No Password - Links are Interchangeable

Free Download Deploying Python Applications On Google Cloud Platform Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.11 GB | Duration: 2h 35m From Training to Cloud: Deploying Machine Learning Models on GCP with Python What you'll learn Explore key platform services like Google Compute Engine (GCE), App Engine (GAE), Kubernetes Engine (GKE), Cloud Run, and Cloud Functions Determine the most suitable service for each type of application Train and evaluate a CNN model, including creating a Python project locally that's ready for deployment Deploy your machine learning application across multiple GCP services, learning to configure environments and manage resources Prevent unnecessary costs by properly cleaning up resources after deployment Requirements Basic knowledge of Python and machine learning (prior experience with neural networks is a plus) Familiarity with web development concepts (optional but recommended) Description Learning to implement machine learning models in production is a critical skill for data scientists who want to move beyond theoretical analysis and create practical business impact. While building models is essential, it is during deployment that these solutions come to life, becoming accessible to end users and integrating into real-world systems. Mastering this phase allows data scientists to ensure the scalability of their solutions, monitor performance in dynamic environments, and collaborate effectively with development and operations teams. Additionally, understanding the full lifecycle-from training to cloud deployment-enhances professional relevance, positioning data scientists as strategic players capable of delivering tangible value from conception to operation.This introductory course is designed for developers, machine learning enthusiasts, and data professionals who want to learn how to deploy their first AI applications on the web using Google Cloud Platform (GCP). Through a hands-on approach, you will be guided from training a convolutional neural network (CNN) for image classification to deploying the model on scalable cloud services. The course includes an introduction to key GCP services such as Google Compute Engine (GCE), App Engine (GAE), Kubernetes Engine (GKE), Cloud Run, and Cloud Functions, enabling you to compare and choose the best option for your project.In the first stage, you will set up your local environment: import libraries (like TensorFlow/Keras), train and evaluate your CNN model, and create a simple Python application to integrate with the trained model. Next, you will learn how to configure GCP and deploy to different services.Ideal for cloud computing beginners and professionals looking to put machine learning models into production. By the end, you will have deployed a functional web application for image classification in the cloud, mastering the full development cycle-from model training to deployment on Google's professional services. Overview Section 1: Introduction Lecture 1 Course content Lecture 2 Course materials Lecture 3 Technical terms Lecture 4 Google Cloud Platform services 1 Lecture 5 Google Cloud Platform services 2 Section 2: Preparing the application Lecture 6 Importing the libraries Lecture 7 Loading the dataset Lecture 8 Creating and training the model Lecture 9 Model evaluation Lecture 10 Creating a local project Lecture 11 Creating a Python app 1 Lecture 12 Creating a Python app 2 Section 3: Deploying Python app on GCP Lecture 13 Preparing Google Cloud Platform Lecture 14 Deploy on Google Compute Engine (GCE) 1 Lecture 15 Deploy on Google Compute Engine (GCE) 2 Lecture 16 Deploy on Google App Engine (GAE) Lecture 17 Deploy on Google Kubernetes Engine (GKE) Lecture 18 Deploy on Cloud Run Lecture 19 Deploy on Cloud Run Functions Lecture 20 Avoid charges: cleaning the environment Section 4: Final remarks Lecture 21 Final remarks Lecture 22 BONUS Cloud computing beginners looking to take their first steps with GCP,Data scientists and Python developers aiming to deploy machine learning models in production Homepage: https://www.udemy.com/course/deploying-python-applications-on-google-cloud-platform/ Rapidgator https://rg.to/file/00a6c63e5c2293cb99ce99a49e5d290a/rgwkh.Deploying.Python.Applications.On.Google.Cloud.Platform.part1.rar.html https://rg.to/file/59b2715b8fdf626a5263b8187b43be66/rgwkh.Deploying.Python.Applications.On.Google.Cloud.Platform.part2.rar.html Fikper Free Download https://fikper.com/koeDkzEzO6/rgwkh.Deploying.Python.Applications.On.Google.Cloud.Platform.part1.rar.html https://fikper.com/Wa6ItfkWqU/rgwkh.Deploying.Python.Applications.On.Google.Cloud.Platform.part2.rar.html No Password - Links are Interchangeable

Free Download Complete 2025 Python Bootcamp - Learn Python from Scratch Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 9h 31m | Size: 8.58 GB Master Python Programming from Scratch: Build Real-World Projects and Become Job-Ready in 2025 What you'll learn Master Python Basics: Learn Python programming fundamentals, including variables, data types, loops, and conditionals. Work with Real-World Data: Gain hands-on experience using Python libraries like Pandas and NumPy for data analysis. Build Projects from Scratch: Develop practical Python projects, such as calculators, games, and automation scripts. Understand Object-Oriented Programming (OOP): Learn how to design classes, objects, and implement inheritance in Python. Automate Tasks with Python: Use Python to simplify repetitive tasks, including file handling and web scraping. Requirements No prior programming experience is required - this course is designed for complete beginners. A computer (Windows, macOS, or Linux) with an internet connection. Basic computer skills, such as using a web browser and installing software. A willingness to learn and a curious mindset! Description Welcome to Complete 2025 Python Bootcamp: Learn Python from Scratch! This course is your ultimate starting point to master Python and become a confident coder-whether you're a complete beginner or looking to sharpen your skills.In this comprehensive, step-by-step course, you'll start with the basics and gradually progress to advanced topics, learning how to build real-world applications along the way. Python is one of the most versatile and in-demand programming languages, used in fields like web development, data science, machine learning, automation, and more. By the end of this course, you'll have the skills and knowledge to take on professional coding challenges and start your career in tech.What you'll learn:Python programming fundamentals, including variables, loops, conditionals, and data types.How to build Python projects from scratch, including games, automation scripts, and web applications.Object-Oriented Programming (OOP) concepts and how to apply them to real-world problems. Debugging and problem-solving techniques to write clean, efficient code.Practical tips and techniques to prepare for coding interviews and real-world development tasks.Why Take This Course?Beginner-Friendly: No prior programming experience is required.Project-Based Learning: Gain hands-on experience by building projects that showcase your skills.Real-World Applications: Learn Python in the context of actual use cases, from web scraping to automating tasks.Career-Focused: Equip yourself with the skills needed to land your first job or advance your career in tech.Whether you want to automate daily tasks, create web apps, or explore data science, this course is the perfect place to start your Python journey. Join me, and let's transform you from a Python novice to a confident coder ready for the job market! Who this course is for Complete Beginners: Individuals with no prior programming experience who want to start their journey in Python and coding. 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Free Download Playwright Python - Pytest with Behave BDD & Live Projects Published: 3/2025 Created by: Rahul Arora MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 116 Lectures ( 17h 37m ) | Size: 8 GB Playwright Python / Pytest Training - Covers all major features, AI Integration, live projects and E2E Frameworks What you'll learn Playwright Automation with Python and Pytest framework All basics till major features of Playwright using the Python CORE End to End Playwright tutorials with Live projects Many frameworks and live projects covered including Data Driven, Keyword Drive, Hybrid, Page Objects, Behave BDD etc Requirements No programming experience needed, You will learn everything you need to know. Complete Python CORE is covered from the scratch for the beginners Description Playwright Python: Comprehensive Training in Microsoft Playwright with Python and Behave BDDMaster the end-to-end implementation of automation using Playwright, Pytest and Python CORE through detailed training lectures. Gain hands-on experience by working on live projects and building robust frameworks such as:Data-Driven Keyword-Driven Hybrid Page Object Model (POM)Behave BDDThis course is one of the most comprehensive Playwright Python courses in the industry, updated with the latest tools and techniques. You'll learn to:Perform complete automation on dynamic, live websites.Handle complex and dynamic web elements.Integrate Playwright with API calls.OpenAI Integration, Building AI Element Finder & AI Image DetectionAdditionally, beginners will be guided through Python CORE concepts, starting from scratch, making this course ideal for both novices and experienced professionals.Why Playwright?Unlike Selenium and other automation tools, Playwright is entirely open-source and free to use. 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Free Download Linkedin - OpenAI API for Python Developers Released: 02/2025 Duration: 2h 18m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 422 MB Level: Intermediate | Genre: eLearning | Language: English Ever since the launch of ChatGPT and the release of new modeling languages by OpenAI in 2022, there has been a growing interest within the wider tech community to explore the potential of multiple applications of generative AI in software and web development. In this course, join instructor Sandy Ludosky to get up to speed quickly with the latest tools and technologies, with hands-on training and exercise challenges along the way. Learn how to use generative AI to create next-generation Python applications, including the OpenAI API, which allows you to build and customize your own AI assistant chatbot directly within an application. Sandy shows you how to start experimenting with other generative models such as DALL-E and the Whisper Audio API as well as how to extend LLM capabilities with function calling and build a custom-knowledge chatbot using LangChain, embeddings, and ChromaDB. Homepage: https://www.linkedin.com/learning/openai-api-for-python-developers Fileaxa https://fileaxa.com/rsuwrbliek1n/mgtdw.Linkedin..OpenAI.API.for.Python.Developers.rar TakeFile https://takefile.link/ju21nnvml5r7/mgtdw.Linkedin..OpenAI.API.for.Python.Developers.rar.html Rapidgator https://rg.to/file/35df7739aa44f6fe02a95a4a95c662dd/mgtdw.Linkedin..OpenAI.API.for.Python.Developers.rar.html Fikper Free Download https://fikper.com/OxETBZ7qlX/mgtdw.Linkedin..OpenAI.API.for.Python.Developers.rar.html No Password - Links are Interchangeable

Free Download Coursera - Data-Oriented Python Programming and Debugging Specialization Released 3/2025 By Anthony Whyte, Paul Resnick, Elle O'Brien - University of Michigan MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 147 Lessons ( 18h 20m ) | Size: 7 GB Write and systematically debug Python code. . Learn to develop readable and reproducible code in Python while investigating, manipulating, and analyzing real-world data using Python libraries. What you'll learn Effective use of modules, functions, and object methods in data-driven computing. Competent independent debugging and self-help skills in Python. Proficient programming with common data structures such as arrays and DataFrames using libraries like NumPy and pandas. 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Free Download Udemy - Learn Hypothesis Testing With Python Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.93 GB | Duration: 7h 57m to succeed in your career What you'll learn Learn avout visualisations, how to code them in Python, and how they enhance the presentation of your work. Learn anout the measure of central tendency, which is composed of the mean, median, and mode of a dataset. Learn about the measure of dispersion, which calculates the spread and standard deviation of a dataset. Learn about the various measures of association and some of the statistical tests that it is comprised of. Learn about probability theory and how it is an integral part of hypothesis testing. Learn about probability distributions and four popular distributions that are used in hypothesis testing. Learn about the central limit theorem, whict is integral to the study of statistics. Learn about confidence intervals, which are an important concept in hypothesis testing. Learn about hypothesis testing and how to perform them. Learn about difference of means tests and how to perform them. Requirements The learner should have a basic understanding of statistics. The learner should have a basic understanding on the Python programming language. Description In today's data-driven world, learning hypothesis testing is essential for several reasons, to include:1. Informed Decision-Making: Hypothesis testing helps individuals make decisions based on data rather than intuition or guesswork. Whether it's in business, healthcare, education, or everyday life, making decisions backed by statistical evidence ensures more accurate and reliable outcomes.2. Critical Thinking: Understanding hypothesis testing fosters critical thinking skills. It encourages individuals to question assumptions, analyze data rigorously, and draw conclusions based on empirical evidence. This skill is valuable in evaluating the credibility of information and avoiding biases.3. Professional Advantage: Many professions, such as data analysis, scientific research, marketing, and finance, require a solid understanding of hypothesis testing. Mastering this skill can enhance career prospects and open doors to opportunities in fields that rely on data analysis and evidence-based decision-making.4. Enhanced Research Skills: Hypothesis testing is a fundamental aspect of scientific research. By learning how to formulate and test hypotheses, individuals can contribute to advancing knowledge in various domains, from medicine to social sciences. It also enables them to critically assess research studies and their findings.5. Policy and Program Evaluation: Hypothesis testing is crucial for evaluating the effectiveness of policies, programs, and interventions. Governments and organizations use it to determine whether initiatives are producing the desired outcomes and to make data-informed decisions for improvements.6. Empowerment in Daily Life: Understanding hypothesis testing empowers individuals to interpret data presented in news, reports, and studies. It helps them make informed choices about personal health, finances, and other aspects of life by discerning valid conclusions from misleading claims.7. Technological Integration: With the rise of big data and artificial intelligence, hypothesis testing has become even more relevant. It forms the backbone of machine learning models and algorithms, enabling the extraction of meaningful insights from vast datasets.8. Reduction of Misinformation: In an era of information overload, knowing hypothesis testing helps combat misinformation. It equips individuals with the tools to critically evaluate the validity of claims and distinguish between scientifically sound information and pseudoscience.In summary, learning hypothesis testing equips individuals with the skills needed to navigate a complex and data-rich world. It promotes informed decision-making, critical thinking, professional development, and a deeper understanding of the world around us.in this course the student will learn how to conduct several hypothesis testing scenerious using the general purpose language, Python. the student will learn about:-1. Visualisation techniques that are important in statistical research, with a special emphasis on hypothesis testing.2. Specific staistical measurements that are important when carrying out a hypothesis test.3. the theory of probability and distribution, with a special emphasis ob the distributions that are used in hypothesis testing.4. the student will learn the Python code of a multitude of practice problems in probability, confidence intervals,hypothesis testing, and difference in means testing. Overview Section 1: Introduction Lecture 1 Introduction Section 2: Charts Lecture 2 charts Lecture 3 line chart Lecture 4 bar chart Lecture 5 pie chart Lecture 6 scatter plot Lecture 7 box plot Lecture 8 histogram Lecture 9 QQ plot Section 3: Measure of central tendency Lecture 10 measure of central tendency Section 4: Measure of dispersion Lecture 11 measure of dispersion Section 5: Measure of association Lecture 12 measure of association Lecture 13 pearson correlation coefficient Lecture 14 spearman rank coefficient Lecture 15 chi2 test of independence Lecture 16 cramers v Lecture 17 odds ratio Lecture 18 linear regression Lecture 19 contingency coefficient Lecture 20 special considerations Lecture 21 logistic regression Section 6: Probability theory Lecture 22 probability theory Section 7: distributions theory Lecture 23 distribution theory Lecture 24 symmetrical distribution Lecture 25 left skewed distribution Lecture 26 right skewed distribution Section 8: Probability distributions Lecture 27 probability distributions Lecture 28 normal distribution Lecture 29 binomial distribution Lecture 30 poisson distribution Lecture 31 t distribution Lecture 32 summary of distributions Section 9: Central limit theorem Lecture 33 central limit theorem Section 10: Practice problems using the normal distribution Lecture 34 loaves of bread Lecture 35 test scores Lecture 36 heights Lecture 37 male heights Lecture 38 manufacturing Lecture 39 sandwiches Section 11: Practice problems using the binomial distribution Lecture 40 dice rolls Lecture 41 tax returns Lecture 42 light bulbs Lecture 43 sports Lecture 44 customer service Lecture 45 pass or fail Section 12: Practice problems using the Poisson distribution Lecture 46 convenience store Lecture 47 coffee shop Lecture 48 defective parts Lecture 49 traffic accidents Lecture 50 help desk Lecture 51 hotel bookings Section 13: Practice poblems with the t distribution Lecture 52 test scores Lecture 53 researcher Lecture 54 drug trials Lecture 55 diet Lecture 56 machines Section 14: Confidence intervals Lecture 57 confidence intervals Lecture 58 house prices and sales Lecture 59 ceo management succession plan Lecture 60 defective batteries Lecture 61 political pollster Lecture 62 teaching methods Section 15: Hypothesis tests Lecture 63 hypothesis testing Lecture 64 bottles Lecture 65 miles per gallon Lecture 66 batteries Lecture 67 software Lecture 68 drugs Lecture 69 men's mba ages Lecture 70 tea or coffee? Lecture 71 masks Lecture 72 lunch Section 16: Difference in means tests Lecture 73 difference in means tests Lecture 74 olympian heights Lecture 75 male and female heights Lecture 76 company salaries Lecture 77 blood pressure Lecture 78 ages of men and women mba students Section 17: End of course Lecture 79 Congratulations for completing the course This course is intended for researchers who would like to know how to perform hypothesis tests.,This course is intended for students would would like to learn more about statistics.,This course is intended for Python programmers wou would like to know more about the statistical and scientific libraries that can be used with the language. 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Free Download Python for Beginners - Learn to Code and Prepare for the PCEP Published: 3/2025 Created by: Manoloiu Ionut-Alexandru MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 16 Lectures ( 1h 51m ) | Size: 383 MB Python Made Easy: Learn to Code from Zero What you'll learn The fundamentals of Python programming from scratch. Core programming concepts applicable to any language. Writing clean, efficient, and optimized Python code. Problem-solving techniques with real-world coding exercises. Requirements A computer with Internet access Basic computer literacy Willingness to learn Description Course DescriptionThis course provides a comprehensive introduction to Python programming, designed for individuals with little to no prior coding experience. Whether you are looking to start a career in programming, automate tasks, or simply explore the world of coding, this course will equip you with the essential skills needed to write efficient and well-structured Python programs.Throughout the course, you will gain a solid understanding of Python's core concepts, learning how to write, debug, and optimize code through a hands-on approach. Each topic is explained in a structured and easy-to-follow manner, ensuring that learners can apply their knowledge to real-world scenarios. Additionally, this course will help you understand the types of questions found on the PCEP (Certified Entry-Level Python Programmer) exam, preparing you for certification if you choose to pursue it.What you will learn:The fundamentals of Python, including variables, data types, and operatorsControl flow structures such as loops, conditionals, and functionsWorking with lists, dictionaries, and sets to manage data efficientlyFile handling, user input, and error handling in Python programsWriting clean, maintainable, and optimized code following best practicesDebugging and troubleshooting errors effectivelyPractical coding exercises to reinforce concepts and improve problem-solving skillsA structured approach to understanding PCEP exam-style questionsWho Should Take This Course?Beginners with no prior programming experience who want to learn PythonIndividuals looking to switch careers and enter the field of programmingStudents and professionals who want to enhance their technical skillsAnyone interested in developing a strong foundation in Python programming for future learning opportunitiesThose who plan to take the PCEP certification exam and need guidance on key concepts and question formatsThis course is designed to be interactive and engaging, with hands-on coding exercises, real-world examples, and step-by-step explanations to ensure a thorough understanding of the concepts. By the end of this course, you will have the confidence to write Python programs independently and be well-prepared to tackle PCEP-style exam questions.Enroll today and take the first step toward mastering Python programming and preparing for the PCEP certification! Who this course is for Anyone who wants to learn Python from scratch. People looking to switch careers into programming. Beginners interested in software development, automation, or data-related fields. Professionals from other industries wanting to add Python to their skill set. Beginner programmers who want to pass PCEP-30-02 Homepage: https://www.udemy.com/course/python-for-beginners-learn-to-code-and-prepare-for-the-pcep/ Rapidgator https://rg.to/file/3b91ff95f51d3db18e3bed30fd468959/xlevv.Python.for.Beginners.Learn.to.Code.and.Prepare.for.the.PCEP.rar.html Fikper Free Download https://fikper.com/vsuFYWTlAk/xlevv.Python.for.Beginners.Learn.to.Code.and.Prepare.for.the.PCEP.rar.html No Password - Links are Interchangeable

Free Download Business Statistics - Sampling Methods with Python and Excel Published: 3/2025 Created by: datascience Anywhere,G Sudheer MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 19 Lectures ( 1h 1m ) | Size: 379 MB Learn Sampling Techniques with Hands on Python and Excel - Random, Stratified, Cluster, and More! What you'll learn Introduction to Business Statistics - Key concepts and importance of sampling. Convenience Sampling - Quick and easy data collection. Simple Random Sampling - Equal chance selection for all data points. Systematic Sampling - Selecting every 'nth' data point. Stratified Sampling - Dividing data into subgroups for better representation. Cluster Random Sampling - Random selection of entire groups. Hands-on Sampling in Python - Implement techniques using pandas and NumPy. Sampling in Excel - Generate samples using Excel's built-in statistical tools. Python vs. Excel for Sampling - Strengths, differences, and best use cases. Real-World Applications - How businesses use sampling for decision-making. Practical Exercises & Case Studies - Apply concepts with industry-relevant examples. Requirements Familiarity with Python and Excel (basic knowledge is sufficient). Python installed on your computer (Anaconda or standalone Python). Excel installed on your system (any version with data analysis tools). Willingness to learn and apply sampling techniques in real-world scenarios. Description Unlock the power of Business Statistics by mastering sampling techniques with Python and Excel! This course provides a practical, hands-on approach to understanding statistics and its applications in real-world data analysis.What You'll Learn:Introduction to Statistics - Understand key statistical concepts and why sampling is crucial for data-driven decision-making.Types of Sampling Methods:Convenience Sampling - Quick and easy data collection.Simple Random Sampling - Every data point has an equal chance of being selected.Systematic Sampling - Selecting every 'nth' data point from a dataset.Stratified Sampling - Dividing data into meaningful subgroups before sampling.Cluster Random Sampling - Random selection of entire groups instead of individuals.Key Highlights:Hands-on Implementation in Python - Apply each sampling method using pandas and NumPy for real-world datasets.Sampling in Excel - Learn how to generate and analyze samples using Excel's built-in statistical tools.Comparing Python vs. Excel Approaches - Explore the strengths, differences, and best use cases for each tool.Real-World Business Applications - Learn how businesses use sampling to improve decision-making, reduce costs, and optimize strategies.Practical Exercises and Case Studies -Relevant examples to solidify your understanding.By the end of this course, you'll be confident in using Python and Excel for statistical sampling, enabling you to make better, data-driven decisions. Who Should Enroll?Business Analysts, Data Enthusiasts, and Students.Professionals looking to enhance their data analytics skills.Anyone interested in statistical methods and data science.Join now and take your business statistics skills to the next level! Who this course is for Data enthusiasts and students interested in statistical sampling methods. Researchers and academics who want to apply sampling techniques in their studies. Beginners in data science seeking practical experience with Python and Excel. Homepage: https://www.udemy.com/course/business-statistics-sampling-methods-with-python-and-excel/ Rapidgator https://rg.to/file/cea6d3c31835d2a35fc878ee730679a8/jmtec.Business.Statistics..Sampling.Methods.with.Python.and.Excel.rar.html Fikper Free Download https://fikper.com/BnTKjaGdwN/jmtec.Business.Statistics..Sampling.Methods.with.Python.and.Excel.rar.html No Password - Links are Interchangeable

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Free Download Linkedin - Creating Advanced AI Applications with Python, APIs, and GitHub Models Released 03/2025 With Jonathan Fernandes MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 17m 6s | Size: 177 MB Learn how to create AI applications with advanced LLM tooling that integrates with third-party APIs using GitHub Models. Course details GitHub Models lets you build AI features and products that integrate with third-party APIs. In this course, instructor Jonathan Fernandes shows you how to create an AI application using advanced large language model (LLM) tooling and GitHub Models. Experiment with LLM tooling such as function calling to determine the best LLM for the task, and then start designing and building your own prototype. Test out your new skills along the way in the hands-on coding challenges at the end of each section. By the end of this course, you'll be equipped with the skills you need to create, test, validate, and deploy an AI-powered app that works effectively with a third-party API. Homepage: https://www.linkedin.com/learning/creating-advanced-ai-applications-with-python-apis-and-github-models TakeFile https://takefile.link/rvhx29isn0qj/qsugy.Linkedin..Creating.Advanced.AI.Applications.with.Python.APIs.and.GitHub.Models.rar.html Rapidgator https://rg.to/file/48c072e87e288501065844b0e4cd3123/qsugy.Linkedin..Creating.Advanced.AI.Applications.with.Python.APIs.and.GitHub.Models.rar.html Fikper Free Download https://fikper.com/F6SUnRwatG/qsugy.Linkedin..Creating.Advanced.AI.Applications.with.Python.APIs.and.GitHub.Models.rar.html : No Password - Links are Interchangeable

Free Download Learn Python Programming - A Complete Bootcamp Published: 3/2025 Created by: Dr. Reza Shahin MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 298 Lectures ( 23h 54m ) | Size: 13.3 GB Python Bootcamp: From Fundamentals to Mastery - Build a Strong Foundation in Programming What you'll learn Understand Python syntax and basic operations. Implement loops and conditional statements effectively. Use functions for modular and reusable code. Perform mathematical operations using math modules. Work with sets, tuples, lists, and dictionaries for data storage. Apply object-oriented programming (OOP) principles. Utilize classes, inheritance, and polymorphism in OOP. Implement basic data structures for efficient problem-solving. Explore Python modules for enhanced functionality. Requirements No programming required. You will learn everything you need Description Welcome to the Python Bootcamp, a comprehensive hands-on course designed to help you master Python programming from the ground up. Whether you are a complete beginner or someone looking to refine your coding skills, this course is structured to provide a step-by-step learning experience covering fundamental concepts to advanced programming techniques.Why Take This Course?Python is one of the most popular programming languages in the world, known for its simplicity, readability, and versatility. It is widely used in web development, data science, artificial intelligence, automation, and software engineering. This course will not only teach you Python syntax and core programming concepts but also help you develop the problem-solving skills necessary to write efficient and scalable code.Who Should Take This Course?Absolute beginners who want to learn programming from scratch.Students and professionals looking to build a strong foundation in Python.Software engineers, data analysts, and AI enthusiasts who want to expand their skill set.Anyone interested in automation, web development, or scripting using Python.What You Will Learn1. Python BasicsUnderstanding Python syntax and execution flow.Variables, data types, and operators.String manipulation and formatting.2. Control Flow: Loops and Conditional StatementsImplementing if-else conditions for decision-making.Using for and while loops for repetition.Understanding nested loops and logical operators.3. Functions and Modular ProgrammingDefining and calling functions in Python.Function parameters, return values, and scope.The importance of code reusability and modular programming.4. Math Modules and Built-in FunctionsPerforming mathematical operations using the math module.Exploring built-in functions for calculations and data manipulation.5. Data Collections: Sets, Tuples, Lists, and DictionariesUnderstanding lists and their operations (sorting, indexing, slicing).Using tuples for immutable collections.Leveraging sets for unique elements and mathematical operations.Working with dictionaries for key-value data storage.6. Object-Oriented Programming (OOP)Understanding classes and objects in Python.Implementing encapsulation, inheritance, and polymorphism.Creating real-world applications using OOP concepts.87 Python Data Structure Modules and PackagesImporting and using built-in Python modules such as namedtuple, chainmap, counter, ordereddictionaries, heapq, and deque,.Creating and organizing your own modules.Understanding the Python package management system.What Makes This Course Unique? * Comprehensive Curriculum - Covers everything from basic syntax to advanced OOP and data structures. * Hands-on Learning - Practice coding with real-world exercises and projects. * Step-by-Step Approach - Each topic builds on the previous one for easy learning. * No Prior Experience Required - Designed for absolute beginners. * Practical Applications - Learn how to write clean, efficient, and reusable Python code.By the end of this course, you will have the skills and confidence to build Python applications, automate tasks, and explore more advanced areas like data science, AI, or web development. * Join this bootcamp and start your Python programming journey today! Who this course is for Absolute beginners who want to learn programming from scratch. Students and professionals looking to build a strong foundation in Python. Software engineers, data analysts, and AI enthusiasts who want to expand their skill set. Anyone interested in automation, web development, or scripting using Python. 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