This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.
Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, from the face detection in our phones and the streams of social media we consume to picking restaurants, partners, and movies. It has also become indispensable to many empirical sciences, including physics, astronomy, biology, and the social sciences.
Scikit-learn has emerged as one of the most popular toolkits for machine learning, and is now widely used in industry and academia. The goal of this tutorial is to enable participants to use the wide variety of machine learning algorithms available in scikit-learn on their own data sets, for their own domains.
This tutorial will comprise an introductory morning session and an advanced afternoon session. The morning part of the tutorial will cover basic concepts of machine learning, data representation, and preprocessing. We will explain different problem settings and which algorithms to use in each situation. We will then go through some sample applications using algorithms implemented in scikit-learn, including SVMs, Random Forests, K-Means, PCA, t-SNE, and others.
In the afternoon session, we will discuss setting hyper-parameters and how to prevent overfitting. We will go in-depth into the trade-off of model complexity and dataset size, as well as discussing complexity of learning algorithms and how to cope with very large datasets. The session will conclude by stepping through the process of building machine learning pipelines consisting of feature extraction, preprocessing and supervised learning.
- What is machine learning? (Sample applications)
- Kinds of machine learning: unsupervised vs supervised.
- Data formats and preparation.
- Supervised learning: Interface
- Supervised learning: Training and test data
- Supervised learning: Classification
- Supervised learning: Regression
- Unsupervised Learning: Unsupervised transformers
- Unsupervised Learning: Preprocessing and scaling
- Unsupervised Learning: Dimensionality reduction
- Unsupervised Learning: Clustering
- Summary : Estimator interface
- Application: Classification of digits
- Methods: Unsupervised learning
- Application : Eigenfaces
- Methods: Text feature abstraction, bag of words
- Application : Insult detection
- Summary : Model building and generalization
- Cross-Validation
- Model Complexity: Overfitting and underfitting
- Complexity of various model types
- Grid search for adjusting hyperparameters
- Basic regression with cross-validation
- Application : Titanic survival with Random Forest
- Building Pipelines: Motivation and Basics
- Building Pipelines: Preprocessing and Classification
- Building Pipelines: Grid-searching Parameters of the feature extraction
- Application : Image classification
- Model complexity, learning curves and validation curves
- In-Depth: Linear Models
- In-Depth: Kernel SVMs
- In-Depth: trees and Forests
- Learning with Big Data: Out-Of-Core learning
- Learning with Big Data: The hashing trick for large text corpuses