Whenever we explore a new path in technology, we are often stuck with roadmaps and then more roadmaps, watching multiple youtube videos on "roadmap to machine learning" and eventually information overload shows it's magic of inaction and procrastination.

I have gone through this cycle already but have finally come out of it and prepared a plan to learn machine learning, gathered everything researched and put together in a structured way.

Mathematics

You need not to be a mathematician or a pro in math's but definitely need some base knowledge in below topics. Learn the basics and intermediate but don't be stuck here forever.

Topics to cover -

1. Exponents

2. Logarithms

3. Linear Algebra

4. Calculus

5. Probability & Statistics

You can learn all of these topics at one place and that should be more than enough -Khan Academy

Want to go deep, this is one of the most popular resource -

Essence of Linear Algebra - 3Blue1Brown

Essence of Calculus - 3Blue1Brown

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Python

The dominant programming language in the field of machine learning and AI is Python.

Topics to cover -

1. Basics of Python (At least up to functional programming)

2. Packages you should know how to use -

   • Numpy

   • Pandas

   • Matplotlib

   • Seaborn

I found below courses to be useful but should be taken in order -

Python for Machine Learning & Data Science Masterclass

Master statistics & machine learning: intuition, math, code (This one is optional but if you want to get an intuition of under the hood working of mathematics and machine learning, this is a good course to go through.)

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Machine Learning

Finally the topic we should be on, this one is a long list but as we progress down the line, the knowledge will continue to expand and build upon. All of these courses are free until and unless you want certifications.

Machine Learning Specialization (Coursera)

This is a stepping stone and has 3 courses, take all these courses in order -

1. Supervised Machine Learning: Regression and Classification

2. Advanced Learning Algorithms

3. Unsupervised Learning, Recommenders, Reinforcement Learning

Deep Learning Specialization

This specialization will equip you to learn the fundamentals of deep learning with neural networks, it has total 5 courses -

1. Neural Networks and Deep Learning

2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

3. Structuring Machine Learning Projects

4. Convolutional Neural Networks

5. Sequence Models

Below are some more courses and you may take them in any order to further expand your knowledge -

• Neural Networks: Zero to Hero [Andrej Karpathy]

• Hugging Face NLP Course

• Large Language Model Operations (LLMOps) Specialization (Coursera)

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Books

Mathematical Notation

Linear Algebra Done Right

Mathematics for Machine Learning

The Elements of Statistical Learning

Pattern Recognition and Machine Learning

Deep Learning

Probabilistic Machine Learning

Information Theory, Inference and Learning Algorithms

Artificial Intelligence: A Modern Approach

A Probabilistic Theory of Pattern Recognition

Approximate time to get through these material depends on how much time you spend and can anywhere range between 6-9 months.