1. Key Concepts Machine Learning (Janani Ravi, 2021)

1. Course Overview: 1. Course Overview 00:00:00 2. Introducing Machine Learning Concept: 1. Version Check 00:01:56 2. Prerequisites and Course Outline 00:02:12 3. What Is Machine Learning 00:04:15 4. Rule-based Systems 00:11:31 5. Rule-based Systems vs. ML-based Systems 00:15:27 6. Traditional ML Models and Representation ML Models 00:21:17 7. Traditional ML Models vs. Deep Learning ML Models 00:27:27 8. The Machine Learning Mindset 00:29:35 9. Examples of AI in the Real World 00:34:38 3. Identifying Problems Solved Using Machine Learning: 1. Choosing the Right Machine Learning Solution 00:39:35 2. Supervised vs. Unsupervised Learning 00:44:19 3. Specialized ML Problems - Recommendation Systems 00:51:38 4. Specialized ML Problems - Association Rules Learning 00:55:53 5. Specialized ML Problems - Reinforcement Learning 00:57:55 6. Identifying Characteristics of Good ML Problems 01:00:06 7. Framing a Machine Learning Solution 01:07:00 4. Applying Machine Learning to Complex Data: 1. Applying ML to Text Image and Speech Data 01:13:05 2. Applying Machine Learning to Text Data 01:15:22 3. Preprocessing Text Data 01:22:49 4. Applying Machine Learning to Image Data 01:26:25 5. The Intuition Behind CNNs 01:31:55 6. Applying Machine Learning to Speech Data 01:35:33 5. Formulating a Simple Machine Learning Solution: 1. Quick Overview of Linear Regression 01:43:24 2. The Machine Learning Workflow 01:47:49 3. Demo - Exploring and Understanding the Salary Dataset 01:50:47 4. Demo - Dealing with Outliers and Missing Values 01:56:22 5. Demo - Performing Simple Linear Regression 02:01:05 6. Summary and Further Study 02:05:55