Section 2
Machine Learning Workflow
8. Section Overview
05:12 (Preview)
9. Data Preparation, Representation and Model Selection
10. Loss Functions
11. Training, Optimisation & Hyper-parameter Tuning
12. Exploring If Models are Any Good - Training Curves
13. Is My Model any Good - Validation Plots
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