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Core Concepts

Core Concepts

Explore a diverse array of learning materials, covering the essentials of ML and including university-level courses instructed by AI professionals. These resources provide a fundamental grasp of vital principles in the realm of ML and AI.

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  • Hands on Machine learing (opens in a new tab) by Aurélien Géron, you'll find a practical approach to machine learning. Dive into ML, grasp essential concepts, work on practical applications, and explore potential R&D directions, all while diving into real-world code examples.

  • Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python (opens in a new tab) is a comprehensive guide to using Python for data science, with a focus on scikit-learn for machine learning and PyTorch for deep learning. It covers topics like model evaluation, hyperparameter optimization, and deep learning, providing code examples for hands-on learning.

  • Pattern Recognition and Machine Learning (opens in a new tab) by Christopher M. Bishop is an insightful exploration of the core concepts in machine learning. This comprehensive guide delves into algorithms, offering a thorough understanding of pattern recognition and its applications in the evolving landscape of artificial intelligence.

  • An Introduction to Statistical Learning (opens in a new tab): As the extent and scale of gathering data expand across nearly every domain, a profound understanding of statistical learning has become indispensable. This book offers a comprehensive and accessible exploration of essential concepts in statistical learning, catering to individuals seeking to harness modern tools for data analysis. Widely recognized as the "Bible of Machine Learning," this book is suitable for a broad audience interested in leveraging contemporary methods for insightful data interpretation.

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