About Course
This course is a continuation of Machine Learning I, where you learnt the fundamentals of machine learning with a significant focus on supervised machine learning. The focus of this course will be unsupervised machine learning and introducing deep learning in the final week to get you up to speed with the fundamental workings of nascent and matured AI models currently taking the tech space like ChatGPT, Stable Diffusion and the rest.
Here is a breakdown of what you will cover in this course:
- Week 6:Dimensionality Reduction Methods
- Week 7:Clustering
- Week 8:Time Series Analysis
- Week 9:Neural Networks
- Week 10:Capstone and Conclusion
Acknowledgements and Attribution
This course is attributed to Jake VanderPlas’ Python Data Science Handbook, Data Ranger’s playlist on Time Series Analysis, Andrew NG’s tutorials on Deep Learning Concepts and MIT Open Course on Introduction to Deep Learning. We have added videos to the course to help make harder concepts simpler to understand. Finally, you have notes by Chris Aloo and Zindua technical team shared on Slack or on the resources
Course Content
6.1 Principal Component Analysis
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Introducing Principal Component Analysis
00:00 -
PCA as dimensionality reduction
00:00 -
PCA for visualization: Hand-written digits
00:00 -
PCA as Noise Filtering
00:00 -
Principal Component Analysis Summary
00:00
6.2 Advanced PCA
6.3 Manifold Learning
6.4 More Manifolds
6.5 Dimensionality Reduction Weekly Project
7.1 k-Means Clustering
7.2 Gaussian Mixture Models
7.3 Kernel Density Estimation
7.4 Hierarchical Clustering
7.5 Clustering Weekly Project
8.1 Introduction to Time Series Analysis
8.2 Time Series – Basic Time Series Models
8.3 ARCH and GARCH Models
8.4 Time Series Forecasting in Python
8.5 Time Series Weekly Project
9.1 Introduction to Neural Networks and Deep Learning
9.2 Convolutional Neural Networks
9.3 Sequential Models
9.4 Deep Generative Modelling
9.5 Deep Learning Weekly Project
10.0 Conclusion and Next Steps
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