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Learn Machine Learning Like a GENIUS and Not Waste Time

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