Python and Data Science. Learn Python and Data Science.

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This course is designed for beginners who want to get started with programming in Python, one of the most popular and accessible languages today. No previous experience is necessary. In one session, students will learn the basics of programming and will be able to create their first Python applications. With clear explanations, practical exercises, and real-world examples, this course is an ideal gateway to exploring computer science and coding. Online Course: Introduction to Python - Programming Basics and Simple Applications Duration : - 60 minutes: intensive format to learn fundamental concepts. - 90 minutes: In-depth format with more time for practical examples and interactive questions. --- Educational objectives At the end of the course, participants will be able to: 1. Understand the basics of programming, including the concepts of variables, data types, conditional structures, and loops. 2. Write and run simple Python scripts. 3. Manipulate inputs and outputs to interact with the user. 4. Solve practical problems with short, functional programs. --- Course syllabus 1. Introduction (10-15 min) - Presentation of Python: Why this language? - Installation and configuration of a programming environment (IDLE, Visual Studio Code, or Jupyter Notebook). - First program: *"Hello, World!"*. 2. Python Basics (20-30 min) - Variables and data types (integers, strings, lists). - Mathematical operations and data manipulation. - Conditional structures (if/else) and loops (for/while). 3. Practical exercises (20-35 min) - Write a program to calculate the sum of two numbers provided by the user. - Create a simple application, like a currency converter or password generator. - Bonus for 90 min format: Fixed a more complex issue involving lists or loops. 4. Q&A and conclusion (5-10 min) - Summary of the concepts covered. - Suggestions for resources to continue learning. - Feedback on the course. --- Teaching methodology - Interactive and engaging: the course combines theoretical explanations with practical exercises. - Personalized approach: Sessions are tailored to the needs of participants. Students are encouraged to ask questions at any time. - Learning by doing: examples and exercises are designed to help you assimilate concepts quickly. --- Target audience This course is ideal for: - Complete beginners in programming. - Students or professionals wishing to acquire skills in Python for their personal or professional projects. - Anyone curious to discover a powerful and versatile tool for solving problems. --- Benefits of this online course - Teaching by an experienced and passionate trainer. - Interactive sessions adapted to your learning pace. - Access to educational materials and exercises to continue practicing after the course. Join us now to discover programming and its applications using Python! ---

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This Python course teaches you the basics of programming in Python, including variables, data types, operators, control structures (if, for, while), functions, and lists. You will also learn how to manipulate data with libraries such as Pandas and NumPy, create graphs and visualizations with Matplotlib and Seaborn, and even develop simple web applications with Flask or Django.

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Welcome to "Machine Learning with Python and PyTorch: Practical Hands-on Training," a beginner-friendly course designed to introduce you to the exciting world of machine learning using two of the most popular tools in the industry: Python and PyTorch. This course focuses on practical, hands-on learning, ensuring you gain the skills needed to start building your own machine learning models. #### Course Objectives: - **Introduction to Machine Learning:** Understand the basic concepts and principles of machine learning. - **Python Programming for Machine Learning:** Learn Python programming essentials tailored for machine learning applications. - **PyTorch Fundamentals:** Get acquainted with PyTorch, a powerful and flexible deep learning framework. - **Practical Experience:** Gain hands-on experience by working on real-world projects and exercises. - **Model Building and Evaluation:** Learn to build, train, and evaluate various machine learning models. #### Course Outline: 1. **Introduction to Machine Learning:** - What is machine learning? - Types of machine learning: supervised, unsupervised, and reinforcement learning - Applications of machine learning in different industries 2. **Python Programming Essentials:** - Introduction to Python programming - Data structures and libraries (NumPy, Pandas) - Basic data manipulation and visualization (Matplotlib, Seaborn) 3. **Getting Started with PyTorch:** - Introduction to PyTorch and its ecosystem - Setting up your environment and installation - Understanding tensors and basic tensor operations 4. **Building Your First Machine Learning Model:** - Data preprocessing and preparation - Splitting data into training and testing sets - Building a simple linear regression model with PyTorch 5. **Training and Evaluating Models:** - Understanding the training process - Loss functions and optimization algorithms - Evaluating model performance using metrics 6. **Advanced Models and Techniques:** - Introduction to neural networks - Building and training a neural network with PyTorch - Exploring convolutional neural networks (CNNs) for image classification 7. **Practical Projects and Applications:** - Hands-on projects to reinforce learning - Real-world applications and case studies - Tips and best practices for successful machine learning projects 8. **Next Steps in Your Machine Learning Journey:** - Exploring further learning resources - Joining machine learning communities and forums - Preparing for advanced topics and courses #### Who Should Enroll: - Beginners with no prior experience in machine learning - Individuals interested in learning Python programming - Aspiring data scientists and machine learning enthusiasts #### Prerequisites: - Basic computer literacy and familiarity with high school-level mathematics - No prior programming or machine learning experience required #### Course Outcomes: By the end of this course, you will be able to: - Understand the fundamental concepts of machine learning - Write and execute Python code for machine learning tasks - Use PyTorch to build, train, and evaluate machine learning models - Apply your knowledge to real-world problems and projects - Take the next steps in advancing your machine learning skills Join us in "Machine Learning with Python and PyTorch: Practical Hands-on Training" to embark on your journey into the fascinating world of machine learning. Gain the skills and confidence needed to build and deploy your own models, and start making an impact with machine learning today.