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Machine Learning

1. ML Courses

1. Machine Learning Best

What is ML?

“Machine Learning” by DeepLearning.AI is a foundational online course taught by Andrew Ng on Coursera. It is one of the most popular and beginner-friendly machine learning courses.

Course Overview
  • Focus: Covers the basics of machine learning with a practical, hands-on approach.
  • Language: Uses Python and Octave/MATLAB for coding exercises.
  • Prerequisites: Basic knowledge of linear algebra, probability, and programming is helpful but not mandatory.
Key Topics
  1. Supervised Learning (Linear regression, logistic regression, neural networks, support vector machines)
  2. Unsupervised Learning (K-means clustering, principal component analysis)
  3. Machine Learning Best Practices (Bias-variance tradeoff, error analysis, feature engineering)
Who Is It For?
  • Beginners looking to enter the field of ML and AI
  • Engineers, researchers, and professionals seeking practical ML skills

2. Stanford CS229: Machine Learning

Stanford CS229: Machine Learning is one of the most well-known machine learning courses, taught by Andrew Ng. It covers the theoretical foundations and practical applications of machine learning, focusing on mathematical rigor and real-world implementation.

Course Highlights

  • Core Topics:

    • Supervised learning (linear regression, logistic regression, SVMs, neural networks)
    • Unsupervised learning (PCA, k-means, Gaussian mixtures)
    • Reinforcement learning and Markov decision processes
    • Probabilistic graphical models
    • Debugging ML Models

  • Prerequisites:

    • Strong background in linear algebra, probability, and statistics
    • Good programming skills (usually Python, MATLAB, or Octave)

  • Course Format:

    • Lectures, assignments, and a final project where students apply ML techniques to real-world problems

3. Fast Ai Popular

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

Part 01
  • 1: Getting started
  • 2: Deployment
  • 3: Neural net foundations
  • 4: Natural Language (NLP)
  • 5: From-scratch model
  • 6: Random forests
  • 7: Collaborative filtering
  • 8: Convolutions (CNNs)
  • …
Part 02
  • 9: Stable Diffusion
  • 10: Diving Deeper
  • 11: Matrix multiplication
  • 12: Mean shift clustering
  • 13: Backpropagation & MLP
  • 14: Backpropagation
  • 15: Autoencoders
  • 16: The Learner framework
  • 17: Initialization/normalization
  • 18: Accelerated SGD & ResNets
  • 19: DDPM and Dropout
  • 20: Mixed Precision
  • …

3. Applied Machine Learning

To learn some of the most widely used techniques in ML:

  • Optimization and Calculus
  • Overfitting and Underfitting
  • Regularization
  • Monte Carlo Estimation
  • Maximum Likelihood Learning
  • Nearest Neighbours
  • …

4. Machine Learning with Graphs (Stanford)

To learn some of the latest graph techniques in machine learning:

  • PageRank
  • Matrix Factorizing
  • Node Embeddings
  • Graph Neural Networks
  • Knowledge Graphs
  • Deep Generative Models for Graphs
  • …

5. Probabilistic Machine Learning

To learn the probabilistic paradigm of ML:

  • Reasoning about uncertainty
  • Continuous Variables
  • Sampling
  • Markov Chain Monte Carlo
  • Gaussian Distributions
  • Graphical Models
  • Tuning Inference Algorithms
  • …

6. Deep Learning: CS 182

To learn some of the widely used techniques in deep learning:

  • Machine Learning Basics
  • Error Analysis
  • Optimization
  • Backpropagation
  • Initialization
  • Batch Normalization
  • Style transfer
  • Imitation Learning
  • …

7. Deep Unsupervised Learning

To learn the latest and most widely used techniques in deep unsupervised learning:

  • Autoregressive Models
  • Flow Models
  • Latent Variable Models
  • Self-supervised learning
  • Implicit Models
  • Compression
  • …

8. NYU Deep Learning SP21

To learn some of the advanced techniques in deep learning:

  • Neural Nets: rotation and squashing
  • Latent Variable Energy Based Models
  • Unsupervised Learning
  • Generative Adversarial Networks
  • Autoencoders
  • …

9. CMU Neural Networks for NLP

To learn the latest neural network based techniques for NLP:

  • Language Modeling
  • Efficiency tricks
  • Conditioned Generation
  • Structured Prediction
  • Model Interpretation
  • Advanced Search Algorithms
  • …

10. Multilingual NLP

To learn the latest concepts for doing multilingual NLP:

  • Typology
  • Words, Part of Speech, and Morphology
  • Advanced Text Classification
  • Machine Translation
  • Data Augmentation for MT
  • Low Resource ASR
  • Active Learning
  • …

11. Advanced NLP

To learn advanced concepts in NLP:

  • Attention Mechanisms
  • Transformers
  • BERT
  • Question Answering
  • Model Distillation
  • Vision + Language
  • Ethics in NLP
  • Commonsense Reasoning
  • …

12. Deep Learning for Computer Vision

To learn some of the fundamental concepts in CV:

  • Introduction to deep learning for CV
  • Image Classification
  • Convolutional Networks
  • Attention Networks
  • Detection and Segmentation
  • Generative Models
  • …
Deep Learning for Computer Vision youtube.com
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving car...

13. Deep Reinforcement Learning

To learn the latest concepts in deep RL:

  • Intro to RL
  • RL algorithms
  • Real-world sequential decision making
  • Supervised learning of behaviors
  • Deep imitation learning
  • Cost functions and reward functions
  • …

14. Full Stack Deep Learning

To learn full-stack production deep learning concepts:

  • ML Projects
  • Infrastructure and Tooling
  • Experiment Managing
  • Troubleshooting DNNs
  • Data Management
  • Data Labeling
  • Monitoring ML Models
  • Web deployment
  • …