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Course Description & Lesson Notes
Official Video Description:
Learn Machine Learning Like a GENIUS and Not Waste Time
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I just started my own Patreon, in case you want to support!
Patreon Link: https://www.patreon.com/c/InfiniteCodes
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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
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Welcome to the step-by-step video guide for Learn Machine Learning Like a GENIUS and Not Waste Time taught by Infinite Codes. This tutorial provides a comprehensive walkthrough designed to take you from foundational principles to practical implementation.
💡 Key Topics Covered in This Course:
- Core Fundamentals & Setup: Understanding the workspace, essential tools, and initial setup for Learn Machine Learning Like a GENIUS and Not Waste Time.
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- Best Practices & Key Shortcuts: Time-saving workflows, keyboard shortcuts, and industry-standard recommendations.
- Troubleshooting & Common Pitfalls: How to avoid common beginner errors and optimize your workflow for peak efficiency.
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