What to learn in order

1. Personalized Roadmap

Get personalized roadmap for learning Machine Learning, Deep Learning, Reinforcement Learning, Linear Algebra, Optimization, Calculus, Statistics, and Probability. This site shows how concepts from all of these courses relates to each other. You just set a goal for learning and it offers you a course to learn that thing in quickest way possible. click here

2. Giles McMullen-Klein's Roadmap

This is the order that he gave, but I think the learning order does not match perfectly. In my opinion, you should gain some intuition in mathematics and machine learning before diving into TensorFlow

Giles McMullen-Klein: youtube channel

Topic	Courses
Python	Python and Jupyter
Experience	Kaggle Learning
TensorFlow	Google Machine Learning Crash Course with TensorFlow APIs
TensorFlow	TensorFlow Tutorials
ML	Web & Book: Machine Learning Mastery, dl books
Math	Youtube Channel: Prof Ghrist Math
Math	Book: Mathematics for Machine Learning
Probability & Statistics	web & book: Intro to Prabability, Statistics, and Random Processes
Statistics	web & Book: Statistics Done Wrong
ML	github & book: Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
Paper	Paper: Pedro Domingos - A few useful things to know about machine learning
Q&A	Question & Answer StackExchange: Data Science, Cross Validated
Pytorch	PyTorch Tutorials
Scikit-learn	Scikit-Learn Tutorials

3. Kooshiar's Roadmap

Kooshiar: youtube channel

Topic	Courses
Math	Khan Academy Linear Algebra, and Deep Learning book by Ian Goodfellow: chapters 2, 3, and 4
Python	beginner, syntax & practise, and python for ml
Computer Science	SQL Tutorial and Data Structures & Algorithms
Engineering	Cloud Computing: source 1 and source 2
Machine Learning	Coursera: Machine Learning Specialization Andrew Ng Coursera (3 Courses)
Artificial Intelligence	Book: Artificial Intelligence by Stuart Russell and Peter Norvig
Deep Learning	Books: Deep Learning Neural Network by Ian Goodfellow and Reinforcement Learning by Richard Sutton
Modern ML Theory: ML Basics	CH5 of DL Ian GoodFellow and Mosh Hamedani: Python ML Tutorial (1h)
Modern ML Theory: DL	CH 6-12 of DL Ian GoodFellow
Modern ML Theory: NLP	Stanford CS224N
Modern ML Theory: RL	DeepMind x UCL
Modern ML Theory: Efficient Transformers	paper
Modern ML Theory: Diffusion Models, LLMs	paper
Tools Tutorials	TensorFlow, PyTorch, and Google Cloud: ML on GCP
Useful Tools	Kaggle Competitions, Google: Free Jupyter Notebook, and Hugging Face: Model Repo

4. ML Study Guide (from AssemblyAI)

Topic	Courses
Math	Khan Academy: Multivariable Calculus, Differential Equations, Linear Algebra, Statistics Probability, and 3Blue1Brown: Essence of Linear Algebra
Python	freeCodeCamp Crash Courses: beginer and intermediate
Foundational Tools	Crash Courses: NumPy, Pandas, and matPlotLib
Machine Learning	Machine Learning Specialization Andrew Ng Coursera (3 Courses) and Machine Learning From Scratch
Hands-on Data Preparation	Kaggle Intro to Machine Learning and Kaggle Intermediate Machine Learning
Practise	Solve Challenges and build your own projects with datasets from Kaggle.com.
Specialize & Create Blog	Specialize in one field (e.g. Computer Vision, NLP, etc.). Look at requirements in corresponding job descriptions and learn those skills. Tip: Create a blog and share tutorials and what you have learned!
Books	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow and Machine Learning with PyTorch and Scikit-Learn

5. Marcel DeSutter's Roadmap (a reddit user) + some of my recoms

you might thinkg some of these courses are old, but they worth it.

Steps	Course(s)
Math: Get general intuition	3blue1brown: Linear Algebra, Calculus, Khan Academy: Multivariable Calculus
Probability Theory	Choose one of these 3 courses: MIT 6-041sc (2013) Probabilistic Systems Analysis And Applied Probability: youtube, course page - MIT 6-012 (2018) Introduction To Probability: youtube, course page, Stanford CS109 (2022) Introduction to Probability for Computer Scientists: youtube, course page
Linear Algebra	MIT 18.06 (2005) Linear Algebra: youtube, course page
Calculus	Calculus with Dr. Marchese: I, II, III
Connect all the previous concepts together	18.065 (2018) Matrix Methods In Data Analysis, Signal Processing, And Machine Learning: youtube, course page
Machine learning basics	Andrew NG's courses on coursera: Machine Learning Specialization, Deep Learning Specialization, Tensorflow in Practice Specialization
Machine Learning	Stanford CS229 ML: Youtube 2008 classic ml, Youtube 2018 updated with new topics, and course page - CORNELL CS4780 ML for Intelligent Systems: youtube, course page
Python	Corey Schafer Youtube Playlists
Programming	Book 1: Python Data Science Handbook and Book 2: Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)

Other Roadmaps

1. roadmap.sh - AI and Data Scientist Roadmap (2024)
2. rentry.org ML roadmap
3. Daniel Bourke ML roadmap (2020)
4. Marcel DeSutter's other roadmap (2021)

Rankings & Reviewes

Greg Hogg's Reviews

Greg Hogg: youtube channel

Ranking The Best Machine Learning Courses & Books

Rank	Courses
Outstanding Content & Free	The Hundred-Page ML Book, Andrew Ng ML Coursera Specialization, An Intro to Statistical Learning: with application in Python/R, and Deep Learning Coursera Specialization
Great Content, but Expensive	Hands-on ML w Sklearn, Keras & TF, Udemy Machine Learning A-Z Py & R, Udemy Python 4 Data Science & ML Bootcamp, and Udemy ML, DS & GAI w Python
Good Content, but Difficult	The Elements of Statistical Learning and Depp Learning by Ian GoodFellow
Decent, but there's better	IBM Machine Learning Certification - IBM Mathematics for ML - All edx courses - Fast.AI Practical Deep Learning for Coders - Google Machine Learning Crash Course - ML Univ of Washington Specialization - Advanced ML HSE University

Data Science Professional Certifications Ranked

Rank	Courses
UNBEATABLE	Andrew Ng ML Coursera Specialization, Deep Learning Coursera Specialization, and IBM Data Science Coursera Professional Certification
GREAT AND IT'S IN PYTHON	IBM Introduction to Data Science Specialization, IBM Data Science Fundamentals with Python and SQL, University of Washington Machine Learning Specialization, and University of Michigan Applied Data Science with Python Specialization