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AI
43 topics across 7 chapters
Chapter 1
Foundations of AI
1
AI History & Milestones
2
Mathematics for AI
3
Probability & Statistics for AI
4
Linear Algebra for AI
Chapter 2
Machine Learning
5
Supervised Learning
6
Unsupervised Learning
7
Reinforcement Learning
8
Evaluation Metrics for ML
9
Regularization & Generalization
Chapter 3
Deep Learning
10
Neural Networks Basics
11
Convolutional Neural Networks (CNNs)
12
RNNs & LSTMs
13
Transformers & Self-Attention
14
Regularization in DL
15
Transfer Learning
Chapter 4
Natural Language Processing
16
Text Preprocessing
17
Language Models
18
Tokenization & Encoding
19
Fine-tuning & Transfer in NLP
20
NLP Evaluation & Benchmarks
Chapter 5
Computer Vision
21
Image Processing Basics
22
CV with CNNs
23
Object Detection
24
Image Segmentation
25
Vision Transformers
Chapter 6
AI Ethics & Safety
26
Bias & Fairness in AI
27
Privacy & Security in AI
28
Transparency & Explainability
29
Safety & Risk Management
30
Responsible AI Guidelines
Chapter 7
AI in Practice & Deployment
31
ML Pipelines
32
MLOps Foundations
33
Deploying Models
34
Monitoring & Observability
35
Scaling AI in Production

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