Contents | Introduction- Supervised learning, unsupervised learning and reinforcement learning
- Classification, regression, structural prediction
- Model evaluation: metrics
- Hyperparameter selection, model selection
- Initiation to Scikit-learn
- Data types and methods selection guide
Classification: Introduction with Optical Character Recognition (OCR) - K nearest neighbors’ algorithm (KNN)
- Decision trees
- Ensemble methods
- Support Vector Machines (SVM)
- Results visualization
Classification: Advanced concepts with sentiment analysis - Data preprocessing for learning algorithms
- Dimensionality reduction
- Batchwise training
- Interpretability
Regression - Linear regression
- Non-linear regression with kernel methods
- Outlier detection and handling
- Time series: Challenges, decomposition and predictive methods
- Time series: non-stationary regression and auto-regressive models
Recommendation systems, case study - Collaborative filtering per user
- Collaborative filtering per item
- Advanced concepts and algorithms
Unsupervised learning - Clustering: K-means, hierarchical clustering, density methods
- Dimensionality reduction: PCA, t-SNE,
- Generative models: Introduction to autoencoders and variational autoencoders
Practical debugging guide - Overfitting test
- Data pipelines test
Exploration of alternative metrics |
