🧠 AI Learning Roadmap
Your Step‑by‑Step Guide
🔥
12
day streak
📊 Overall progress
32%
Phase 1Phase 2Phase 3Phase 4Phase 5
📚 Step‑by‑Step Learning Path
📍 Phase 1: Foundations
1. What is AI? History & Context
Understand the evolution and modern landscape of AI.
▶ Watch
2. Linear Algebra & Calculus for AI
Core math concepts used in machine learning.
▶ Watch
3. Python for Data Science & AI
Master NumPy, Pandas, and visualization tools.
▶ Watch
📍 Phase 2: Core AI / ML
4. Supervised Learning (Regression & Classification)
Predictive modeling with labeled data.
▶ Watch
5. Unsupervised Learning (Clustering & PCA)
Finding hidden patterns in data.
▶ Watch
6. Neural Networks & Deep Learning Basics
Architecture of perceptrons and backpropagation.
▶ Watch
📍 Phase 3: Advanced Deep Learning
7. Convolutional Neural Networks (CNNs)
Image recognition and computer vision.
▶ Watch
8. RNNs, LSTMs & Sequence Models
Processing time-series and text data.
▶ Watch
9. Transformers & Attention Mechanisms
The architecture behind modern LLMs.
▶ Watch
📍 Phase 4: Generative AI & LLMs
10. Generative AI & GANs
Creating new data with generative models.
▶ Watch
11. LLMs & Prompt Engineering
Working with models like GPT, Llama, and Claude.
▶ Watch
12. Reinforcement Learning from Human Feedback (RLHF)
Aligning AI with human values.
▶ Watch
📍 Phase 5: Applications & Deployment
13. Building RAG Systems
Retrieval-Augmented Generation pipelines.
▶ Watch
14. AI Agents & Tool Calling
Autonomous agents and function calling.
▶ Watch
15. Deploying AI Models
APIs, Docker, and cloud deployment.
▶ Watch
🧠 AI Learning Dashboard · v1.0
🔥 12 day streak · 32% complete
