How To Self Study AI FAST

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Self Study AI Checklist: Beginner 1. Learn Python --- APIs (v. imp) 2. Learn LLMs 3. Learn Prompt Engineering --- Make a Project (Chatbot, Image Generator, etc) Intermediate 4. Learn Python libraries: -- Numpy -- Pandas -- Matplotlib -- Scikit-learn 5. Learn Maths: -- Calculus -- Linear Algebra -- Probability 6. Learn Statistics: -- Inferential Stats -- Descriptive Stats -- Hypothesis Testing -- Central Limit Theorem -- Distributions -- Confidence Intervals As a CSE grad, most and maybe all of these were taught but my brain was not braining. Lets see how long can I go this time. I dont know if this will be of help tbh, but good video Tina ☺

Lovely and insightful video !! Thanks for sharing out Tina☺

It's a very useful video, first you feel frustrated and lost, but after viewing it, you clear understand AI and get ready for a real work. Thank you and happy next machine learning year, 2024.

🎯 Key Takeaways for quick navigation: 00:00 🧑‍🎓 Introduction to Learning AI - The video introduces the challenge of learning AI and sets the stage for a new learning method. 00:57 🎯 The Renon Method - The Renon Method is introduced as a way to learn AI incrementally by starting with the basics and progressively building knowledge. - It involves creating small AI projects to maintain motivation and enthusiasm. 02:07 🤖 Understanding Machine Learning - An explanation of machine learning is provided, focusing on pattern recognition in data. - The example of a hot dog or not-hot-dog model is used to illustrate machine learning. 03:48 🍕 How AI Models Learn - Details on how AI models, such as the hot dog or not-hot-dog model and ChatGPT, learn from data. - The concept of probabilities and predictions is explained. 05:13 📚 Learning Python and APIs - Recommendations for learning Python basics and using APIs to interact with AI models. - Resource suggestions for Python learning are provided. 07:07 🔢 Learning Math and Statistics - Guidance on essential math and statistics topics for AI, including calculus, linear algebra, and probability. - Resource recommendations for learning these subjects are given. 09:00 🧠 Deep Learning and Specializations - Introduction to deep learning, neural networks, and specialization areas like computer vision and natural language processing. - Resource suggestions for deep learning are shared. 11:25 🛠️ Building AI Projects - Advice on starting to build AI projects, contributing to open-source AI models, and fine-tuning existing models. - Emphasis on practical application and not getting overwhelmed with too many resources. Made wit

Best video I have seen about AI so far. Your video gave me a light when I was lost in the sea of AI.

I've seen just a few videos of yours come across my feed. I have to say they were very helpful. I have a goal to learn AI using free resources and you give a great outline for people who want to learn. Thank you and keep the videos coming!

Yes definitely make more videos like this! Extremely helpful ❤️

I've been looking for a video like that for so long! Thanks you Tina 👌🏻

“Prime numbers are what is left when you have taken all the patterns away. I think prime numbers are like life. They are very logical but you could never work out the rules, even if you spent all your time thinking about them.” ― Mark Haddon, The Curious Incident of the Dog in the Night-Time

Tina, you're a brilliant human being and DS. The best is yet to come. Happy 2024! Thanks!

AWESOME details and breakdown, THANK YOU, YES some of use have ADD but are great IT engineers. learning a vast new tech is an uphill with a weights on, thanks

Super helpful roadmap for learning AI. Your teaching style is fun and amazing. Thanks! ❤

TYSM Tina, this was so well done!

Great, great video with so many actionable resources. Lots of notes taken. I also really appreciate the disclaimer at the end on not treating this vid like a to-do list of all the things we've got to learn. Soothes my perfectionist demon.

Some clarying points and addiontal tips, espeically for job seekers: 1. You only need basic math, you do not need to know a single thing about calc, linear alebgra, etc. if you are not going into research. Of course, the more you know about this the better, but if you just want to make AI products you don't need it. Research is a completley diffrent ball game where you will need at LEAST a master's to so much as get your foot in the door. It also doesn't pay as much as applied jobs (like Machine Learning Engier, Data Engineer, or even higher level Data Analysts). Optional: if you want to skip calc/differnation etc. that's fine but I strongly recomend learning linear algebra as it will make your life much easier. Again, you don't need it to get by but if you're looking for work in this espially it will make your life much easier. 2. The type of statistics you need are APPLIED Statstics (this includes all the types she is talking about, this is more about the rigor you are learning it). If you are going into research you might learn these from a more rigours perspective where you would need to already understand concepts from Calclus and so forth. 3. If you're looking to get a job, a realvent masters defeinely helps but so do quality Certifcations. So Micrsoft, Amazon, Google should be your starting points. ONLy get them from the websites themselves, do not get them from Cousea, Udacity etc. It's okay to learn from these sites, just dont' put them on your resume (some places are okay with this) 4. Get good at programming, espeially in Python. The following is optional: I would also take courses (online for free is fine) in Discrete Math, Data Strucures (a proper course, not just learning what Python's are) and Some kind of Algohrims Course. These will help you problem solve as you need to figure out how to implmeent things. Again, you can get away with not learning this stuff, but it will make your life easier. P.S. This is all just my opinoin from being in the job market and working. I encourge you to do your own resarch.

Best video on ai so far on YouTube...now you got me interested in self learning ai and you just earn a subscriber.🎉❤

I love your videos. This was extremely helpful. Thank you!

First Video that actually makes sense to me thank you so much

Thank you, this is such a fantastic video, full of great resources and really well presented 😊