Introduction: Big Data & Data Science
Reminders on algebra (vectors and matrices) & statistics
Machine Learning: Automatic Learning
- Definition and history
- Examples of machine learning applications
- Modeling a problem in Machine Learning
- Types of learning (Supervised / Unsupervised)
Steps in learning
- Choice of model
- Learning: calculation of model parameters
- Over-learning
- Validation, cross validation, test
- Model comparison criteria
Getting started with Python
- The Jupyter Notebook
- Introduction to Python Programming
- Basic structures and operations in Python
- LAB 1: Getting started with Python
- Data recovery
- Exploration and preprocessing of data (use of Pandas and Numpy libraries)
- Visualization of data (use of the Matplotlib library)
- LAB 2: Data exploration and pre-processing
Learning algorithms
Regression
- Use case: Prediction of selling prices of houses
- Metrics of the regression
- Linear regression
- Principle and functioning
- Cost / loss function
- Optimization function (Gradient Descent algorithm)
- LAB 3: Linear regression
- Regressions: multiple, Ridge, Lasso
- LAB 4: Multiple regression, Ridge & Lasso regression
Classification
- Use case: Detection of spam emails
- Metrics of the classification
- Logistic Regression
- SVM (Vector Support Machine)
- LAB 5: Logistic Regression & SVM
- Decision trees
- Random forests
- LAB 6: Decision Trees and Random Forests
- K-NN (the nearest K neighbors)
- LAB 7: K-NN
Segmentation & clustering
- Use cases: Segmentation of articles
- Distances K-means (K-Means)
- LAB 8: K-averages
- Spectral clustering
- Hierarchical clustering
- LAB 9: Spectral & Hierarchical clustering
Recommendation Systems
- Use case: Recommendation system for an e-commerce site
- Content-based filtering
- LAB 10: Content-based filtering
- Collaborative filtering
- LAB 11: Collaborative filtering
Dimensional reduction
- Use case: example of the Iris data
- PCA (Principle Component Analysis)
Deep learning: Deep learning
- Use case: Image classification
- Multilayer perceptron neuron networks
- Convulsion neural networks
- Recurrent neural networks
- Autoencoders networks