@wozskiyeh9651
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.
@zaire419
Trying out this roadmap March 1st 2023. Will update everyone 6months from then. I’m already a software engineer so I’ll be skipping the coding steps and the math will be refreshers but far from a data scientist or data analyst for that matter. Hope everything works out. See you guys in the future!
@ammo7204
starting this roadmap from today. wish me luck! hope everyone else also achieves their goal.
@Glimmer-t44
Nice, I was struggling to decide what to learn first? This field is so overwhelming for beginners. Thanks for explaining out everything so clearly.
@krim797
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!
@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!
@kevinshen0807
This man just single handedly planned my life, what a legend!
@ahafeezs
Amazing how machine learning algorithm in YouTube works, I was just thinking about ML earlier today and in the evening I got this video recommendation. ☀🔨
@TheOriginalJohnDoe
I think the most underrated part is the math. I myself study Artificial Intelligence in university, which is a bit different and more advanced than simply machine learning. We take 12 courses upfront before starting 'the real deal' machine learning. We learn linear algebra, calculus, bayesian statistics, logic and I absolutely love the way our major is structured in this way, because now that we're doing machine learning, everything makes sense and with this knowledge you really learn on what data you can apply which model. You don't learn that online. They simply say: "for these problems, you simply use these models", which is okay for data scientists, but not for people who study AI themselves within the research field.
@julesf7857
I liked this video and saved it. Made me also notice something about IT people: they don't breathe! I was listening to an IT specialist on TV yesterday, he didn't even listen to the Qs of the interviewer my head hurts its even unsettling
@nicekhan8552
One of the most luxurious pieces of advice I've ever heard ( or watched) Thank you, Patrick.
@andreypopov6166
Thanks for the great learning plan. I would just add that for Multivariable Calculus, Single variable calculus is needed. And as an option instead of "Statistics Probability" i would use an ordered learning path: "Combinatorics -> Probability -> Statistics"
@rons2722
This outline is phenomenal - thank you!
@alexjbriiones
Your suggestion to create a blog is simply genius.
@arthurferreira7997
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.
@aminehadjmeliani72
One of the most luxurious advice I've ever heard ( or watched) Thank you Patrick
@Jaeoh.woof765
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.
@quant_sheep
My leaning map is below,hope it can help anybody i begin my python learning in 12,2022. I quickly read a book in 72h. After that I began to learn on Kaggle. I make my coding skills better(some basic pandas numpy and matplotlib) and learn some basic ml. After that I find that my data analysis skill is not good enough to clean the data. I read the book called python for data analysis. I finished it in 2,2023. From 3,2023 to now I am reading the ml part of hands on ml. I hope to finish it in next week. This book give us some real world views. After that it’s time to learn the math inside the ml.
@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