Machine Learning with PyTorch and Scikit-Learn

This course offers a comprehensive exploration of machine learning and deep learning using PyTorch and Scikit-Learn. It provides clear explanations, visualizations, and practical examples to help learners build and deploy machine learning models. Ideal for Python developers, it covers the latest trends in deep learning, including GANs, reinforcement learning, and NLP with transformers. Packed with clear explanations, visualizations, and working examples, the course covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks. This course is designed for developers and data scientists with a solid understanding of Python basics, calculus, and linear algebra. It is ideal for those looking to create practical machine learning applications using Scikit-Learn and PyTorch, and deepen their knowledge of advanced deep learning techniques. Throughout this course you will learn to: - Develop machine learning models using Scikit-Learn and PyTorch. - Implement neural networks and transformers for various data types. - Apply best practices for model evaluation and tuning. This course is based on material written by an expert author, bringing the depth of a book into a more engaging, interactive format. The core content is delivered through clear, structured text you can read at your own pace, supported by short videos and quizzes to highlight key ideas and test your understanding. By combining the strengths of book learning with interactive assessments, you get the best of both worlds: the depth and clarity of an author’s expertise, plus the flexibility to revisit, practice, and reinforce concepts whenever you need.