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In this course, you will learn :
- The fundamentals of AI, its relationship to machine learning, and the various types of algorithms you should be familiar with.
- Learn the rules, best practises, and infrastructure for building great AI products that users can rely on.
- You'll go over some real-world case studies of AI applications and dive deep into Ethical AI.
- Overview of AI's current state and future prospects.
Syllabus :
1. The Fundamentals
- Basics of AI
- The Highs and Lows of AI
- Understanding Machine Learning
- Supervised Learning
- Supervised Learning: Algorithms and Business Use Cases
- Unsupervised Learning
- Unsupervised Learning: Algorithms and Business Use Cases
- Reinforcement Learning
- Deep Learning
- Convolutional Neural Networks: What, When, How and Where
- Recurrent Neural Networks: What, When, How and Where
- Natural Language Processing: Word Embeddings
- Natural Language Processing: BERT
- Recommendation Systems
- Transfer Learning
- Evaluation Metrics
2. AI in Practice
- Creating AI Solutions That Users Trust
- Creating Great AI Products: The Rules
- Key ML Lessons
- AI Infrastructure: Overview
- AI Infrastructure: AI Frameworks
- AI Infrastructure: Cloud Services to Create AI Solutions
3. Real Case Studies
- Starbucks: The Millions of AI Infused Cups of Coffee
- Netflix: Using AI To Give Us Better Entertainment
- American Express: AI and Credit Cards
- AI for Wildlife Conservation
4. Responsible AI
- Fairness
- Interpretability
- Privacy
- Security
- Addendum
