Machine Learning Guide

A guide covering Machine Learning including the applications, libraries and tools that will make you better and more efficient with Machine Learning development.

Note: You can easily convert this markdown file to a PDF in VSCode using this handy extension Markdown PDF.

Machine Learning/Deep Learning Frameworks.

Table of Contents

1. Learning Resources for ML

   • Developer Resources
   • Courses & Certifications
   • Books
   • YouTube Tutorials

2. ML Frameworks, Libraries, and Tools

   • LLMs Training Frameworks
   • Tools for deploying LLMs
   • Running Large Language Models (LLMs) Locally

3. Algorithms

4. PyTorch Development

5. TensorFlow Development

6. Core ML Development

7. Deep Learning Development

8. Reinforcement Learning Development

9. Computer Vision Development

10. Natural Language Processing (NLP) Development

11. Bioinformatics

12. CUDA Development

13. MATLAB Development

14. C/C++ Development

15. Java Development

16. Python Development

17. Scala Development

18. R Development

19. Julia Development

Learning Resources for ML

Back to the Top

Machine Learning is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

Developer Resources

Back to the Top

• Natural Language Processing (NLP) Best Practices by Microsoft

• The Autonomous Driving Cookbook by Microsoft

• Azure Machine Learning - ML as a Service | Microsoft Azure

• How to run Jupyter Notebooks in your Azure Machine Learning workspace

• Machine Learning and Artificial Intelligence | Amazon Web Services

• Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances

• AI & Machine Learning | Google Cloud

• Using Jupyter Notebooks with Apache Spark on Google Cloud

• Machine Learning | Apple Developer

• Artificial Intelligence & Autopilot | Tesla

• Meta AI Tools | Facebook

• PyTorch Tutorials

• TensorFlow Tutorials

• JupyterLab

• Stable Diffusion with Core ML on Apple Silicon

Courses & Certifications

Back to the Top

• Machine Learning by Stanford University by Andrew Ng | Coursera

• AWS Training and Certification for Machine Learning (ML) Courses

• Machine Learning Scholarship Program for Microsoft Azure | Udacity

• Microsoft Certified: Azure Data Scientist Associate

• Microsoft Certified: Azure AI Engineer Associate

• Azure Machine Learning training and deployment

• Learning Machine learning and artificial intelligence from Google Cloud Training

• Machine Learning Crash Course for Google Cloud

• Machine Learning Courses Online | Udemy

• Machine Learning Courses Online | Coursera

• Learn Machine Learning with Online Courses and Classes | edX

Books

Back to the Top

• Introduction To Machine Learning (PDF)

• Artificial Intelligence: A Modern Approach by Stuart J. Russel and Peter Norvig

• Deep Learning by Ian Goodfellow, Yoshoua Bengio, and Aaron Courville

• The Hundred-Page Machine Learning Book by Andriy Burkov

  • Hundred-Page Machine Learning Book on GitHub

• Machine Learning by Tom M. Mitchell

• Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran

• Machine Learning: An Algorithmic Perspective, Second Edition

• Pattern Recognition and Machine Learning by Christopher M. Bishop

• Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

• Python Machine Learning: A Technical Approach to Machine Learning for Beginners by Leonard Eddison

• Bayesian Reasoning and Machine Learning by David Barber

• Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald

• Machine Learning in Action by Ben Wilson

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

• Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller & Sarah Guido

• Machine Learning for Hackers: Case Studies and Algorithms to Get you Started by Drew Conway and John Myles White

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

• Distributed Machine Learning Patterns - Book (free to read online) + Code

• Real World Machine Learning [Free Chapters]

• An Introduction To Statistical Learning - Book + R Code

• Elements of Statistical Learning - Book

• Think Bayes - Book + Python Code

• Mining Massive Datasets

• A First Encounter with Machine Learning

• Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan

• A Probabilistic Theory of Pattern Recognition

• Introduction to Information Retrieval

• Forecasting: principles and practice

• Introduction to Machine Learning - Amnon Shashua

• Reinforcement Learning

• Machine Learning

• A Quest for AI

• R Programming for Data Science

• [Data Mining