Homework - Machine Learning
- Week 1
- Giving Computers the Ability to Learn from Data
- Training Machine Learning Algorithms for Classification
- Week 2
- A Tour of Machine Learning Classifiers Using Scikit-learn
- Building Good Training Sets – Data Preprocessing
- Week 3
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Eva. and Hyperparameter Tuning
- Week 4
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Week 5
- Embedding a Machine Learning Model into a Web Application
- Predicting Continuous Target Variables with Regression Analysis
- Week 6
- Working with Unlabeled Data – Clustering Analysis
- Training Artificial Neural Networks for Image Recognition
- Week 7
- Parallelizing Neural Network Training with Theano
- Thinking in Machine Learning
- Midterm (Sample Questions)
- Week 8
- Tools and Techniques
- Turning Data into Information
- Week 9
- Models – Learning from Information
- Linear Models
- Week 10
- Neural Networks
- Features – How Algorithms See the World
- Week 11
- Learning with Ensembles
- Design Strategies and Case Studies
- Week 12
- Unsupervised Machine Learning
- Deep Belief Networks
- Week 13
- Stacked Denoising Autoencoders
- Convolutional Neural Networks
- Week 14
- Semi-Supervised Learning
- Text Feature Engineering
- Ensemble Methods
- Additional Python Machine Learning Tools