Foundations of Machine Learning
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Section 1: Introduction to ML
- Welcome Message (Don't Skip!)
- What is Machine Learning
- How to Get Queries Resolved
- Garbage-In Garbage-Out
- Broad Types of ML Problems Part-1
- Broad Types of ML Problems Part-2
- Broad Types of ML Problems Part-3
- Marketing and Sales Use Cases
- Logistics & Production, HR, Customer Support Use Cases
- Course Review
- What ML Can and Cannot Do
- Data Science vs ML vs AI vs Deep Learning vs Statistical Modeling
- Quiz - Introduction to ML
Section 2: ML Project Workflow
- Introduction to ML Project Workflow
- Discover
- Design
- Develop
- Testing
- Deploy
Section 3: ML Models
- Interpreting ML Models
- Interpreting ML Models Part-1
3.Interpreting ML Models Part-2
- How to Validate ML Models
- Need for Validation Sample
- ML Terminology You Need to Know - Part 1 - Supervised vs Unsupervised Learning
- ML Terminology You Need to Know - Part 2 - Independent vs Dependent Variables
- ML Terminology You Need to Know - Part 3 - More Terms
Section 4: Special Topics
- What is Ensemble Learning
- Reinforcement Learning Intuition
- Basic Statistical Concepts Part-1
- Basic Statistical Concepts Part-2
- Role of Significance Tests
- Course Review