AI &amp; Machine Learning

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StatQuest: Neural Networks Clearly Explained

Beginner-friendly explanation of NNs

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Neural Networks - 3Blue1Brown

Chapter 1: But what is a Neural Network?

🎓Courses(5)

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CS229: Machine Learning

Learn Machine Learning fundamentals with Stanford's CS229 course by Andrew Ng. Explore key AI concepts and algorithms.

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Practical Deep Learning for Coders

Learn practical deep learning from the ground up with Jeremy Howard's free course! Build amazing AI models, no PhD required.

Machine Learning

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Machine Learning

This Course Machine Learning offers a comprehensive, hands-on introduction to building and deploying machine learning models using Python. It is designed for learners with a foundational understanding of Python programming and familiarity with basic data analysis concepts. The course begins with a quick review of essential Python libraries such as NumPy, pandas, and Matplotlib, which form the foundation for data manipulation and visualization in data science. Learners are then introduced to core machine learning concepts, including supervised learning techniques such as classification and regression. The course places a strong emphasis on practical implementation using the scikit-learn package, enabling learners to build, train, and evaluate various models effectively. It also covers artificial neural networks and delves into deep learning through TensorFlow, where participants apply regression and classification techniques on real-world datasets. With the growing importance of unstructured data, the course explores neural network-based models for analyzing text and image data, equipping learners to handle diverse data types. By the end of the course, participants will have the ability to design and implement machine learning workflows, drawing actionable business insights from both structured and unstructured data. This skill set supports careers in data analysis, data engineering, and data science across industries.

Deep Learning

"Deep Learning" gives an overview of the basic types of neural networks and how to train them. Starting with Multi-Layer Perceptrons (MLPs), the course covers the most important training tricks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), as well as Transformers, and gives an outlook on effective training and efficient fine-tuning of large multi-modal models.

📚Books(2)

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Hands-On Machine Learning

Hands-On Machine Learning - A book resource

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ML

ML - A book resource

🌐Websites(9)

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Hugging Face

Free models, datasets, and courses for NLP/ML

Towards Data Science

Articles and tutorials on AI/ML

Distill.pub

Interactive articles on ML concepts

Papers with Code

ML research papers with code implementations

fast.ai

Free practical deep learning courses and resources

deeplearning.ai

DeepLearning.ai is an AI education platform offering practical online courses and programs in deep learning and AI, including the Deep Learning Specialization, with curricula, certificates, and learning resources for students and professionals.

fast.ai

fast.ai is a nonprofit offering free, practical deep learning courses and resources built around the fastai library (on PyTorch). The site includes course materials, notebooks, tutorials, and up-to-date research insights to help learners build real-world AI applications.

ai.org

ai.org is a centralized resource hub for learning about artificial intelligence. It provides introductory articles, tutorials, glossaries, and reference materials on AI and machine learning concepts and applications.

paperswithcode.com

Papers with Code is a research hub that links machine learning papers to their open-source code, datasets, and benchmark leaderboards, helping you explore, reproduce, and compare methods across tasks.