Section 2
Machine Learning Workflow
8. Section Overview
05:12 (Preview)
9. Data Preparation, Representation and Model Selection
30:35 (Preview)
10. Loss Functions
23:12
11. Training, Optimisation & Hyper-parameter Tuning
17:37
12. Exploring If Models are Any Good - Training Curves
21:47
13. Is My Model any Good - Validation Plots
13:04 (Preview)
Section 3
General Linear Models
14. Section Overview
04:25 (Preview)
15. The Foundations of ML - Curve Fitting
21:39
16. Regression Walkthrough
23:47
17. Underfitting and Overfitting
21:50
18. Controlling ML Models - Regularisation in Practise
16:28
19. Exploring Simple Relationships - Correlations
11:28
20. Finding Nonlinear Relations - Predictive Power Score
11:09
21. Correlation & PPS Walkthrough 📂
24:27
22. From Regression to Classification - Logistic Regression
20:27
23. Logistic Regression in Wild
15:48
24. Looking through the Right Lens - Principle Component Analysis
16:46
25. Looking in the Right Direction - Exploring PCA
21:23
26. Conclusion, Certificate, and What Next?
02:41
2. Section Overview
Introduction to Machine Learning

In this quick video explainer we walk you through the outline of our first section of the course.

The primary learning outcomes are

  • Understand High Level Concepts behind Machine Learning.

  • Learn about some exciting areas where ML is having a meaningful impact in Science, Engineering & Technology.

  • Understand the different types of methods and tasks that are performed using machine learning algorithms.

  • Introduce you to your first supervised and unsupervised learning algorithms : K Nearest Neighbour and K Means

  • Walk through your first python examples, with some real world data sets.

Next Lesson
3. Supervised Vs Unsupervised Learning - Explainer