Section 2
Machine Learning Workflow
8. Section Overview
05:12 (Preview)
9. Data Preparation, Representation and Model Selection
30:35
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
14. Conclusion, Certificate, and What Next?
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.

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3. Supervised Vs Unsupervised Learning - Explainer