#Lesson 11: Treasure Hunt in ML/AI: Map for Noobs, Intermediate and Advanced Learners

Learning Pathways: AI Expert Roadmap, Daniel Bourke’s , Daniel Formoso’s

AI Basics: ML Crash Course, Elements of AI

Python: Geek4Geeks(basics), Corey Schafer, AutomatingTheBoringStuff

Free Courses: fast.ai (I, II), CS50-Harvard, ML-Stanford, mlcourse,

Paid Courses: deeplearning, ZerotoMastery, AppliedAICourse

• Linear Algebra: 3Blue1Brown, MIT(OCW), Rachel Thomas

• Statistics & Probability: MIT(edX), KhanAcad

• Calculus: 3Blue1Brown

Coding Platforms: DataCamp, DataQuest

Bootcamps: FullstackDL,

Books: 100PgML, MLEngineering, MLProblems, Scikit-TensorF

Discover: MadewithML, AIUseCases,

Earn Credentials: AI/ML/Data Science (Completely Online):

• (i) Universities(Masters Degrees): Stanford, ImperialCollegeLondon, Texas

• (ii) Universities(Certificates): MITx, Stanford,

• (ii) Online Platforms: Kaggle (Become GrandMaster), Workera, Udacity(Nanodegree: ML Engineer)

Earn Credentials: AI Product Manager (Completely Online):

• (i) OnlinePlatforms: Udacity(Nanodegree: AI Product Manager), Udemy, Udacity(Nanodegree: Data Product Manager)

• (ii)

• (iii)

Earn Credentials: MS in Analytics (Completely Online):

• (i) Universities(Masters Degrees): Georgia Tech(edX),

• (ii) Online Platforms:

  

1. [Youtube] Daniel Bourke’s (2.5Hr) walkthrough of a roadMap for learning ML/AI

2. [Notion] Daniel Bourke’s Notion List(41 resources) for learning ML/AI

3. [Map] Daniel Bourke’s playground with a mindmap for ML/AI terrain

4. [Github] Daniel Bourke’s summary of links for his ML/AI mindmap research

5. [Github] Daniel Formoso’s Cheatsheet/Mindmap on the terrain of ML/AI

6. [Github] Daniel Formoso’s Mindmap for DL/AI

7. [Github] AI Expert Roadmap

1. Industry Experts: Cassie Kozyrkov

2. Proven Instructors: Jeremy Fast.ai, Andrew Ng, Daniel Bourke (Ex-Uber, independent ML/AI Instructor)

• https://arxiv.org/

• https://madewithml.com/

• https://paperswithcode.com/

• AWS Sagemaker for Deployment

Non-related Bonus Content: Architectural 

1. The Death of Microservices

2. Applied AI/ML Vs Theoretical AI/ML