Course can be found in Coursera
Notebook for quick search can be found in my blog SSQ
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Week 1 Introduction
- Regression. Case study: Predicting house prices
- Classification. Case study: Analyzing sentiment
- Clustering & Retrieval. Case study: Finding documents
- Matrix Factorization & Dimensionality Reduction. Case study: Recommending Products
- Capstone. An intelligent application using deep learning
- Familiar with Ipython notebook and Sframe
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Week 2 Regression Predicting House Prices
- Linear Regression
- Adding higher order effects
- Evaluating overfitting via training/test split
- Adding other features
- Other regression examples
- Implement Linear Regression model with different several features
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Week 3 Classification Analyzing Sentiment
- Classifier applications
- Linear classifiers
- Decision boundaries
- Training and evaluating a classifier
- What’s a good accuracy?
- False positives, false negatives, and confusion matrices
- Learning curves: How much data do I need?
- Class probabilities
- Implement Logistic Regression model with different several features