How To Self Study AI FAST

A video to learn AI skills for my short attention span friends who keep giving up on learning this field.

It's about learning, coding, and generally how to get your sh*t together c:

It's a FREE weekly 1hr livestream about AI & tech topics eg. how to build a GPT, how to build AI products, jobs in the era of AI etc.

Check it out if you're interested in learning AI & data skill, then applying them to real freelance projects!

🖱️Links mentioned in video
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🔗Affiliates
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My SQL for data science interviews course (10 full interviews):

365 Data Science:

Check out StrataScratch for data science interview prep:

🎥 My filming setup
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⏰Timestamps
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00:00 intro

📲Socials
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🎥Other videos you might be interested in
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How I consistently study with a full time job:

How I would learn to code (if I could start over):

🐈‍⬛🐈‍⬛About me
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Hi, my name is Tina and I'm an ex-Meta data scientist turned internet person!

📧Contact
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youtube: youtube comments are by far the best way to get a response from me!

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Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel! :)

[Tomb-]

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 ☺

[arunkkumark]

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

[fishfish]

“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

[kamertonaudiophileplayer]

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.

[kenchang]

Hey, that's me...a distracted, short attention span learning. Thanks for this.

[xllAyato]

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

[Dylan.Fortin]

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

[MedHelp]

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!

[ilah]

Yes definitely make more videos like this! Extremely helpful ❤️

[TinaHuang]

🎯 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 with HARPA AI

[alexanderhermes]

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

[acmaysnetworker]

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

[binry_dstructr]

That scene where Jian Yang (not Jimmy) built a binary classifier (hot dog vs. not hot dog) but everyone else was expecting a multi-class classifier was absolutely hilarious. Don't forget he ended up selling his app for millions!

[DiscoverHudsonValley]

Awesome! I have learned a good portion of the recent ai and don’t have a short attention span but still loved this video 👍😎

[georgeimus]

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

[till]

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.

[skane]

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

[millionairesclub]

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

[qpurple]

I just want to say thank you so much! This is the most valuable video I have ever watched on YouTube. Especially including Coursera courses is very thoughtful of you

[jangalang_jangalang]

Automate the boring stuff is an amazing book. Provides alot details while not having the reader become bored after few pages.