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Curriculum
- 10 Sections
- 112 Lessons
- 4 Weeks
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- Section 1: Introduction to ArtificiaI Intelligence & Machine Learning15
- 1.1Lesson 1: What is Artificial Intelligence?
- 1.2Lesson 2: History of Artificial Intelligence
- 1.3Lesson 3: Types of Artificial Intelligence
- 1.4Lesson 4: What is Machine Learning?
- 1.5Lesson 5: AI vs ML vs Data Science
- 1.6Lesson 6: Real-world Examples and Applications
- 1.7Lesson 7
- 1.8Lesson 8
- 1.9Lesson 9
- 1.10Lesson 10
- 1.11Lesson 11
- 1.12Lesson 12
- 1.13Lesson 13
- 1.14Lesson 14
- 1.15Quiz 140 Minutes12 Questions
- Section 2: Foundations of Machine Learning14
- 2.1Lesson 1: How machines learn
- 2.2Lesson 2: Training vs testing data
- 2.3Lesson 3: Features and labels
- 2.4Lesson 4: Model Lifecycle
- 2.5Lesson 5: Overfitting and underfitting
- 2.6Lesson 6:
- 2.7Lesson 7:
- 2.8Lesson 8:
- 2.9Lesson 9:
- 2.10Lesson 10:
- 2.11Lesson 25
- 2.12Lesson 26
- 2.13Lesson 27
- 2.14Quiz 230 Minutes11 Questions
- Section 3: Python for Artificial Intelligence & ML15
- 3.1Lesson 1: Python basics
- 3.2Lesson 2: Machine Learnin libraries
- 3.3Lesson 3: Loading datasets
- 3.4Lesson 4: Data preprocessing
- 3.5Lesson 5: Model training basics
- 3.6Lesson 33
- 3.7Lesson 34
- 3.8Lesson 35
- 3.9Lesson 36
- 3.10Lesson 37
- 3.11Lesson 38
- 3.12Lesson 39
- 3.13Lesson 40
- 3.14Lesson 41
- 3.15Quiz 340 Minutes10 Questions
- Section 4: Supervised Learning11
- Section 5: Unsupervised Learning14
- Section 6: Neural Networks & Deep Learning11
- Section 7: Model Evaluation & OptimizationEa videat maxima irridebat sequor coletur poterimus diligenter labor constanter drusum munus10
- Section 8: AI Applications11
- Section 9: Ethics & Responsible AI10
- Section 10: Case study Project11
Lesson 112
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