Learn Machine Learning Like a GENIUS and Not Waste Time

You won’t learn Machine Learning in 3 months. I don’t know how long it will take you but here I am sharing everything I know to get you started and make it as fast as possible. 

Also Watch: 
All Machine Learning algorithms explained in 17 min https://youtu.be/E0Hmnixke2g 
The Math that make Machine Learning easy (and how you can learn it) https://youtu.be/wOTFGRSUQ6Q
15 Machine Learning Lessons I Wish I Knew Earlier https://youtu.be/espQDESe07w 

Machine Learning Playlist: https://www.youtube.com/watch?v=wOTFGRSUQ6Q&list=PLbdTl8vSSyUDAvDPc1r3j9itciu_kb5vG&ab_channel=InfiniteCodes

Git/Github Playlist:
https://www.youtube.com/watch?v=ZFFtMyOFPe8&list=PLbdTl8vSSyUBJg6PI9AqfJBw8U0y9J3kY&ab_channel=InfiniteCodes 

👇🏻 Links Resources I mention in the Video👇🏻

=== Python ===

My Python for beginners playlist: https://youtube.com/playlist?list=PLbdTl8vSSyUAJid3yaBjqcMrvLwhcM6vf&si=hfFpa4SPgVlNZ9b9

Other tutorials:
Official Course: https://docs.python.org/3/tutorial/index.html 
W3 schools: https://www.w3schools.com/python/
Uni of Helsinki MOOC: https://programming-24.mooc.fi/ 
Khan Academy: https://www.khanacademy.org/computing/intro-to-python-fundamentals 
Or simply type “Python tutorial” into Google :-) 

=== Math ===

Stats & Probability:
https://www.khanacademy.org/math/statistics-probability
https://www.edx.org/learn/probability/harvard-university-introduction-to-probability 

Linear Algebra:
https://www.khanacademy.org/math/linear-algebra 
https://www.edx.org/learn/linear-algebra/the-university-of-texas-at-austin-linear-algebra-foundations-to-frontiers 
3Blue1brown (neat visuals) https://youtu.be/fNk_zzaMoSs?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab 

Calculus:
https://www.khanacademy.org/math/differential-calculus 
3Blue1Brown (amazing visuals!)  https://youtu.be/WUvTyaaNkzM?si=5A_P-RlD5ICvDeQq 


=== Data Tools ===
Jupyter: https://jupyter.org/ 
Numpy: https://numpy.org/doc/stable/user/quickstart.html
Pandas: https://pandas.pydata.org/docs/user_guide/10min.html
Matplotlib: https://matplotlib.org/stable/tutorials/index.html 


=== ML Theory ===

ISL (free PDF) https://www.statlearning.com/ 
ISL Python Playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ 
Coursera ML introduction: https://www.coursera.org/specializations/machine-learning-introduction#courses 


=== ML tools ===

SK Learn: https://scikit-learn.org/stable/tutorial/basic/tutorial.html 
SK Learn algorithm Cheat Sheet: https://scikit-learn.org/stable/tutorial/machine_learning_map/ 

=== Neural Networks / Deep Learning ===

Coursera: https://www.coursera.org/specializations/deep-learning?irclickid=x-U2gpTSJxyLTxPwUx0Mo3EoUkDXeu01jUFXWo0&irgwc=1#courses 
3Blue1Brown (amazing visuals!) https://youtu.be/aircAruvnKk?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi 

Andrej Kaparthy https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ 
Hugging Face NLP course: https://huggingface.co/learn/nlp-course/chapter1/1 

Keras https://keras.io/getting_started/ 

=== Real Projects ===
https://www.kaggle.com/ 
https://github.com/ 

================== Timestamps ================
00:00 - Intro
00:40 - Why learn Machine Learning & Data Science
01:11 - How to learn?
03:25 - Where to start? (Jupyter, Python, Pandas)
05:25 - Your first Data Analysis Project
06:32 - Essential Math for Machine Learning (Stats, Linear Algebra, Calculus)
08:53 - The Core Machine Learning Concepts & Algorithms (From Regression to Deep Learning)
10:16 - Scikit Learn
11:37 - Your first Machine Learning Project
13:24 - Collaborate & Share
13:59 - Advanced Topics
14:22 - Do’s and Don'ts

YouTube

Learn the smart way to master machine learning without wasting time. Focus on the essential skills: Python programming, data analysis with Pandas, core math concepts like statistics and linear algebra, and simple machine learning algorithms. Practice through real projects, not just tutorials, and learn to adapt quickly as technology evolves. Understand the fundamentals deeply before moving to more advanced topics. Collaborate, share your projects, and avoid common pitfalls to maximize your learning efficiency.