Course Overview
Module 1: Foundations of Artificial Intelligence
- 1.1 Introduction to AI Preview
- 1.2 Types of Artificial Intelligence Preview
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
- 2.1 Linear Algebra Preview
- 2.2 Calculus Preview
- 2.3 Probability and Statistics Preview
- 2.4 Discrete Mathematics
Module 3: Python for Developer
- 3.1 Python Fundamentals Preview
- 3.2 Python Libraries
Module 4: Mastering Machine Learning
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
Module 5: Deep Learning
- 5.1 Neural Networks
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
Module 6: Computer Vision
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
Module 8: Reinforcement Learning
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
Module 9: Cloud Computing in AI Development
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
Module 10: Large Language Models
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
Optional Module: AI Agents for Developers
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Tools you will explore
- GitHub Copilot
- Lobe
- H2O.ai
- Snorkel
