Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning Żory

The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts.Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications. Spis treści: 1. FOREWORD 2. DATASETS USED IN THIS BOOK 3. INTRODUCTION 4. DEVELOPMENT ENVIRONMENT 5. MACHINE LEARNING LIBRARIES 6. EXPLORATORY DATA ANALYSIS 7. DATA SCRUBBING 8. PRE-MODEL ALGORITHMS 9. SPLIT VALIDATION 10. MODEL DESIGN 11. LINEAR REGRESSION 12. LOGISTIC REGRESSION 13. SUPPORT VECTOR MACHINES 14. K-NEAREST NEIGHBORS 15. TREE-BASED METHODS 16. NEXT STEPS 17. APPENDIX 1: INTRODUCTION TO PYTHON 18. APPENDIX 2: PRINT COLUMNS O autorze: Oliver Theobald, a technical writer and best-selling author, excels in AI, fintech, and cloud computing. With global experience, he now resides between China and Japan, deepening his expertise in technology. As an instructor, Oliver emphasizes clarity and engagement, stripping away jargon to make complex topics accessible. His courses aim to empower both beginners and professionals with practical skills for success in the tech industry, making learning both effective and enjoyable.