10 Sections
45 Lessons
10 Weeks
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Intro to AI Module: Getting started
6
1.1
Building an AI tool in 5 minutes: A quick demo
1.2
What does the course cover
1.3
Natural vs Artificial Intelligence
1.4
Brief history of AI
1.5
Demystifying AI, Data science, Machine learning, and Deep learning
1.6
Weak vs Strong AI
Intro to AI Module: Data is essential for building AI
4
2.1
Structured vs unstructured data
2.2
How we collect data
2.3
Labelled and unlabelled data
2.4
Metadata: Data that describes data
Intro to AI Module: Key AI techniques
3
3.1
Machine learning
3.2
Supervised, Unsupervised, and Reinforcement learning
3.3
Deep learning
Intro to AI Module: Important AI branches
4
4.1
Robotics
4.2
Computer vision
4.3
Traditional ML
4.4
Generative AI
Intro to AI Module: Understanding Generative AI
10
5.1
The rise of Gen AI: Introducing ChatGPT
5.2
Early approaches to Natural Language Processing (NLP)
5.3
Recent NLP advancements
5.4
From Language Models to Large Language Models (LLMs)
5.5
The efficiency of LLM training. Supervised vs Semi-supervised learning
5.6
From N-Grams to RNNs to Transformers: The Evolution of NLP
5.7
Phases in building LLMs
5.8
Prompt engineering vs Fine-tuning vs RAG: Techniques for AI optimization
5.9
The importance of foundation models
5.10
Buy vs Make: foundation models vs private models
Intro to AI Module: Practical challenges in Generative AI
4
6.1
Inconsistency and hallucination
6.2
Budgeting and API costs
6.3
Latency
6.4
Running out of data
Intro to AI Module: The AI tech stack
7
7.1
Python programming
7.2
Working with APIs
7.3
Vector databases
7.4
The importance of open source
7.5
Hugging Face
7.6
LangChain
7.7
AI evaluation tools
Intro to AI Module: AI job positions
3
8.1
AI strategist
8.2
AI developer
8.3
AI engineer
Intro to AI Module: Looking ahead
2
9.1
AI ethics
9.2
Future of AI
Python Module: Why Python?
2
10.1
Programming Explained in a Few Minutes
10.2
Why Python
The AI Engineer Course 2026: Complete AI Engineer Bootcamp
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