A repository with code and notes from Lazy Programmer's machine leaning courses on Udemy. Each notebook contains a both theory and code. These courses are by far the best I found so far, because they force the student through the theory and into coding an implementation from scratch, instead of only showing the student how to rely on a machine learning API.
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Time Series Analysis, Forecasting, and Machine Learning in Python (course link)
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Linear Programming in Linear Regression in Python (course link)
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Data Science: Supervised Machine Learning in Python (course link)
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Bayesian Machine Learning in Python: A/B Testing (course link)
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Deep Learning in Python (course link)
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Modern Deep Learning in Python (course link)
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Ensemble Machine Learning in Python: Random Forest and AdaBoost (course link)
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Convolutional Neural Networks in Python (course link)
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Cluster Analysis and Unsupervised Machine Learning in Python (course link)
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Unsupervised Deep Learning in Python (course link)
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Data Science: Natural Language Processing (NLP) in Python (course link)
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Hidden Markov Models in Python (course link)
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Artificial Intelligence: Reinforcement Learning in Python (course link)
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Recurrent Neural Networks in Python (course link)
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Deep Learning with Natural Language Processing in Python (course link)
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Advanced AI: Deep Reinforcement Learning in Python (course link)
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Deep Learning: GANs and Variational Autoencoders (course link)
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Deep Learning: Advanced Computer Vision (course link)
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Deep Learning: Advanced NLP and RNNs (course link)
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Recommender Systems and Deep Learning in Python (course link)
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Machine Learning and AI: Support Vector Machines in Python (course link)
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Cutting-Edge AI: Deep Reinforcement Learning in Python (course link)