Foundations of Machine Learning

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Section 1: Introduction to ML

  1. Welcome Message (Don't Skip!)
  2. What is Machine Learning
  3. How to Get Queries Resolved
  4. Garbage-In Garbage-Out
  5. Broad Types of ML Problems Part-1
  6. Broad Types of ML Problems Part-2
  7. Broad Types of ML Problems Part-3
  8. Marketing and Sales Use Cases
  9. Logistics & Production, HR, Customer Support Use Cases
  10. Course Review
  11. What ML Can and Cannot Do
  12. Data Science vs ML vs AI vs Deep Learning vs Statistical Modeling
  13. Quiz - Introduction to ML

Section 2: ML Project Workflow

  1. Introduction to ML Project Workflow
  2. Discover
  3. Design
  4. Develop
  5. Testing
  6. Deploy

Section 3: ML Models

  1. Interpreting ML Models
  2. Interpreting ML Models Part-1 3.Interpreting ML Models Part-2
  3. How to Validate ML Models
  4. Need for Validation Sample
  5. ML Terminology You Need to Know - Part 1 - Supervised vs Unsupervised Learning
  6. ML Terminology You Need to Know - Part 2 - Independent vs Dependent Variables
  7. ML Terminology You Need to Know - Part 3 - More Terms

Section 4: Special Topics

  1. What is Ensemble Learning
  2. Reinforcement Learning Intuition
  3. Basic Statistical Concepts Part-1
  4. Basic Statistical Concepts Part-2
  5. Role of Significance Tests
  6. Course Review
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