Phase 10: AI Specializations β Start HereΒΆ
Go deep in Computer Vision, Natural Language Processing, and AI Agents β the three core specialization tracks.
Three TracksΒΆ
Track |
Directory |
Topics |
|---|---|---|
Computer Vision |
|
CLIP, CNNs, object detection, segmentation |
NLP |
|
NER, translation, summarization, classification |
AI Agents |
|
Tool use, LangGraph, ReAct, multi-agent |
Computer Vision TrackΒΆ
Notebook |
Topic |
|---|---|
|
ResNet, ViT, zero-shot with CLIP |
|
YOLO, DETR, bounding boxes |
|
SAM, DeepLab, pixel-level classification |
|
Stable Diffusion overview |
|
End-to-end production CV pipelines |
|
Multi-scale features, self-supervised learning |
NLP TrackΒΆ
Notebook |
Topic |
|---|---|
|
Sentiment, topic, intent classification |
|
spaCy, transformers-based NER |
|
Helsinki-NLP, MarianMT models |
|
Extractive vs. abstractive summarization |
|
Extractive QA, reading comprehension |
|
Cross-lingual transfer, zero-shot NLP |
AI Agents TrackΒΆ
Notebook |
Topic |
|---|---|
|
LangChain chains, prompts, memory |
|
OpenAI/Anthropic tool/function use |
|
ReAct pattern (Reason + Act) |
|
Stateful agent graphs with LangGraph |
|
Multi-agent systems, coordination |
|
Build and register custom agent tools |
|
Evaluating agent reliability |
PrerequisitesΒΆ
RAG Systems (Phase 08)
Embeddings (Phase 05)
Neural Networks basics (Phase 06) for the CV track
Recommended PathΒΆ
Pick the track most relevant to your goals:
Building products? β AI Agents track first
Working with images? β Computer Vision track
Working with text/documents? β NLP track