How I would learn Machine Learning (if I could start over)

In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources.

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#MachineLearning #DeepLearning

0:00 Introduction
1:01 MATH
1:58 PYTHON PYTHON
2:37 ML TECH STACK ML TECH STACK
3:35 ML COURSES ML COURSES
4:44 HANDS-ON & DATA PREPARATION
5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO
6:16 SPECIALIZE & CREATE BLOG

[cisforcoding]

(Note to Self - How I would learn Machine Learning)
01:00 1. Math: Khan Academy
Recommended Courses:
- Multi-Variable Calculus
- Differential Equations
- Linear Algebra
- Statistics and Probability
02:00 2. Python
Recommended Courses
- FreeCodeCamp: Python in 4-Hours Full Course
- FreeCodeCamp: Intermediate Python in 6-Hours
02:37 3. Machine Learning TECH STACK
Most important Python libraries for Machine Learning, Data Science, and Data Visualization
Optional: Can be picked up later when doing the ML course.
Use for every project, which is why he recommends doing them now to build a base.
Follow a free crash course for now, pick up more advanced concepts later if needed.
- NumPy: Base for everything: Python Engineer - NumPy Crash Course Complete Tutorial
- Pandas: Data handling: Keith Gali - Complete Python Pandas Data Science Tutorial
- MatPlotLib: Visualization: FreeCodeCamp - MatPlotLib Crash Course
The following MachineLearning courses aren't yet needed
- Tensor Flow
- Scikit Learn
- PyCharge ???
03:35 4. Machine Learning Courses
- Machine Learning Specialization by Andrew Ng (Coursera)
- Implement algorithm from scratch using his 'ML from SCRATCH' playlist
- ML from Scratch Playlist by Python Engineer (Assembly AI)
04:45 5. Hands - On & Data Preparation
Kaggle Courses
- Intro to Machine Learning
- Intermediate Machine Learning
05:19 6. Practice & Build Portfolio
Kaggle: Competitions
- They provide lots of datasets, platform to evaluate, and a community.
06:15 7. Specialize & Create Blog
- NLP
- PyTorch / Tensor Flow
- MLOps
06:52 Start a VLOG
- Tutorial
- Share what you've learned
- Share the projects you've built
- Problems faced and how you have solved them
- Write about a topic
07:24 Books
- Machine Learning with PyTorch and SckiKit-Learn by Raschka
- Hands-On Machine Learning with SciKit-Learn, Keras & TensorFlow by Geron

[saremish]

Very effective steps! I have been following this roadmap for the past couple of months, and I am happy with the progress I have made

[WhiteNoises]

This is just what I was looking for! I was overwhelmed with the amount of resources out there, so it is incredibly useful to have a solid roadmap going forward. Thank you!

[rons]

This outline is phenomenal - thank you!

[aminehadjmeliani]

One of the most luxurious advice I've ever heard ( or watched)
Thank you Patrick

[nicekhan]

One of the most luxurious pieces of advice I've ever heard ( or watched)
Thank you, Patrick.

[wozskiyeh]

1. Math 1:00
2. Python 2:00
3. Machine Learning TECH STACK 2:37
4. Machine Learning Courses 3:35
5. Hands - On & Data Preparation 4:45
6. Practice & Build Portfolio 5:19
7. Specialize & Create Blog 6:15

Awesome! Thank you for sharing.

[Glimmer-t]

Nice, I was struggling to decide what to learn first? This field is so overwhelming for beginners. Thanks for explaining out everything so clearly.

[EddieK]

Fantastic. Short, to the point and clear!

[afizs]

Great roadmap Patrick! It would be great if add few examples projects to practise. Most of the ML learners find it challenging to find projects.

[qfrvchd]

This outline is phenomenal - thank you!. This outline is phenomenal - thank you!.

[Jaeoh.woof]

Thanks for the video. I have learned lots of ML-related stuff in the past several months, but I feel like the way I have learned is NOT the the best way. The way you suggested makes more sense.

[pogo]

Awesome. Go for it. Can’t wait to hear updates.

[InwardGaze]

I really value this plan...you don't understand. There's so many people who quit at the jump because people in the industry give very broad steps. This is a very clear plan with flexibility to go even deeper into each resource and step. Also, for starters, you even said 3 months. Some may say that is unrealistic but as a Math major with no CS experience but a heavy interest in AI theoretically, the drive is already there. Learning can't be rushed but it can definitely be integrated quickly with the right resources. I plan on putting at least 10 hours each week into this journey. Thanks again man!

[arthurferreira]

Great video! I was completely lost on how to start learning about AI. I am a finance major, and I realized that if I don't learn it now, I will probably get left behind.

[anatoliyzavdoveev]

Thank you Patrik!!! Amazing intro for ML topic🙏🙏

[leatheljamie]

Youre awesome, no bullshit, litteraly just helping people, thank you.

[debashreewaddadar]

Thank you so much Patrick for your insights and guidance. The links you have provided are really helpful. Thanks again!

[HS-dnuu]

Thanks for the advice. I’m going to apply your approach in my learning. It sounded feasible and well-though 🙏

[ammo]

starting this roadmap from today. wish me luck!

hope everyone else also achieves their goal.