| 1 |
What is AI? |
Definitions, Categories, Applications, Relation to Data Science |
| 2 |
Mathematical Foundations |
Essential Math: Linear Algebra |
| 3 |
Mathematical Foundations |
Essential Math: Probability & Statistics |
| 4 |
Mathematical Foundations |
Essential Math: Calculus Concepts (Gradients) |
| 5 |
Mathematical Foundations |
Essential Math: Optimization |
| 6 |
Data Engineering |
Python, NumPy, Pandas, Matplotlib |
| 7 |
Data Engineering |
Jupyter |
| 8 |
Data Engineering |
Data Engineering: Spark, Hadoop, ETL |
| 9 |
Classical ML |
The ML Workflow |
| 10 |
Classical ML |
Supervised Learning (Regression & Classification) |
| 11 |
Classical ML |
Unsupervised Learning (Clustering & Dimensionality Reduction) |
| 12 |
Classical ML |
Model Evaluation and Validation |
| 13 |
Classical ML |
Feature Engineering and Selection |
| 14 |
Classical ML |
Scikit learn |
| 15 |
Neural Networks |
Introduction to Neural Networks |
| 16 |
Neural Networks |
Training Neural Networks (Backpropagation) |
| 17 |
Neural Networks |
Deep Learning Frameworks: TensorFlow & PyTorch |
| 18 |
Computer Vision |
Convolutional Neural Networks (CNNs) |
| 19 |
Computer Vision |
Transfer Learning for Image Classification |
| 20 |
Computer Vision |
Object Detection and Segmentation |
| 21 |
Natural Language Processing (NLP) |
Introduction to NLP and Text Preprocessing |
| 22 |
Natural Language Processing (NLP) |
Text Representation: Bag-of-Words, TF-IDF, Word2Vec |
| 23 |
Natural Language Processing (NLP) |
Recurrent Neural Networks (RNNs) & LSTMs |
| 24 |
Natural Language Processing (NLP) |
Attention Mechanism and Transformers |
| 25 |
LLM Fundamentals |
Introduction to Large Language Models (LLMs) |
| 26 |
LLM Fundamentals |
The Generative AI Lifecycle |
| 27 |
LLM Applications |
Prompt Engineering, Business Use Cases |
| 28 |
LLM Applications |
Working with LLMs: Embeddings |
| 29 |
LLM Applications |
The LLM Ecosystem: Hugging Face |
| 30 |
Applied AI |
AI Agents (Reasoning & Tools) |
| 31 |
Applied AI |
Retrieval-Augmented Generation (RAG) |
| 32 |
Multi-agent Systems and Agent Architectures |
Reactive, Deliberative, BDI Model |
| 33 |
AI Planning and Reasoning |
Search Algorithms, Goal Trees |
| 34 |
Modern Agent Frameworks |
LangChain, AutoGPT, CrewAI |
| 35 |
AI Ethics |
Bias, Fairness |
| 36 |
AI Ethics |
Privacy, Accountability |
| 37 |
Explainable AI |
SHAP, LIME |
| 38 |
Human-in-the-loop AI |
Augmenting Human Decision-Making |
| 39 |
AI Strategy |
Building an AI Strategy |