If you’re planning to learn machine learning online for free, this post is for you.

Based on content type, I categorized the resources into three groups.

You can learn machine learning by:

Watching

Online Courses

AI For Everyone - Coursera

  • Perfect for beginners
  • No technical background required
  • Overview of AI applications

Machine Learning Course - Stanford

  • Comprehensive introduction
  • Taught by Andrew Ng
  • Mathematical foundations

Deep Learning Specialization

  • Advanced topics
  • Neural networks focus
  • Practical applications

YouTube Channels

3Blue1Brown

  • Visual explanations of complex concepts
  • Neural networks series
  • Mathematical intuition

Sentdex

  • Python machine learning tutorials
  • Practical implementations
  • Real-world examples

Two Minute Papers

  • Latest research summaries
  • Cutting-edge developments
  • Easy-to-understand explanations

Reading

Books (Free Online)

“Elements of Statistical Learning”

  • Comprehensive theoretical foundation
  • Mathematical rigor
  • Advanced concepts

“Pattern Recognition and Machine Learning”

  • Bayesian approach
  • Detailed explanations
  • Academic standard

“Hands-On Machine Learning”

  • Practical approach
  • Python implementations
  • Real-world projects

Blogs and Articles

Towards Data Science (Medium)

  • Industry insights
  • Tutorial articles
  • Case studies

Machine Learning Mastery

  • Step-by-step tutorials
  • Code examples
  • Practical tips

Google AI Blog

  • Latest research
  • Industry applications
  • Technical deep dives

Doing - Hands-on

Programming Platforms

Google Colab

  • Free GPU access
  • Python environment
  • No setup required

Kaggle

  • Competitions and datasets
  • Community notebooks
  • Learning environment

Jupyter Notebooks

  • Interactive coding
  • Data visualization
  • Experimentation

Practice Datasets

Iris Dataset

  • Perfect for beginners
  • Classification problem
  • Small and manageable

Boston Housing

  • Regression problem
  • Real-world data
  • Feature engineering practice

MNIST

  • Image classification
  • Deep learning introduction
  • Benchmark dataset

Projects to Build

  1. Predictive Analytics Dashboard
  2. Recommendation System
  3. Natural Language Processing Tool
  4. Computer Vision Application
  5. Time Series Forecasting Model

Learning Path Recommendation

Phase 1: Foundation (2-3 months)

  • Complete “AI for Everyone” course
  • Learn Python basics
  • Practice with simple datasets

Phase 2: Core Concepts (3-4 months)

  • Take Stanford ML course
  • Implement basic algorithms
  • Work on guided projects

Phase 3: Specialization (3-6 months)

  • Choose focus area (NLP, CV, etc.)
  • Advanced courses
  • Build portfolio projects

Phase 4: Advanced Topics (Ongoing)

  • Deep learning specialization
  • Research papers
  • Contribute to open source

Tips for Success

Set Clear Goals

  • Define what you want to achieve
  • Set realistic timelines
  • Track your progress

Practice Consistently

  • Code every day
  • Work on projects
  • Join communities

Build Projects

  • Apply what you learn
  • Create a portfolio
  • Share your work

Stay Updated

  • Follow AI news
  • Read research papers
  • Attend virtual conferences

Common Pitfalls to Avoid

  • Jumping to complex topics too quickly
  • Focusing only on theory without practice
  • Not working on real projects
  • Trying to learn everything at once

Community and Support

Reddit Communities

  • r/MachineLearning
  • r/LearnMachineLearning
  • r/datasets

Discord Servers

  • AI/ML focused communities
  • Real-time help and discussion
  • Collaboration opportunities

Stack Overflow

  • Technical questions
  • Code debugging
  • Best practices

Learning machine learning for free is absolutely possible with the wealth of resources available online. The key is to balance theory with practice and stay consistent in your learning journey.

Happy learning/coding! 🚀