Advanced Machine Learning with Scikit Learn: Tools and Techniques for Predictive Analytics in Python

O'Reilly Video Series. Free preview (~40min), full series is 3:45h

In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python.

You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification.

Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons.

Engineering Open Source Machine Learning Software

Data Science Summit 2016 invited talk

This talk lays out the principles guiding the design of scikit-learn, focussing on usability and maintainability. I'm also discussing some continuing challenges, like feature creep and increasing complexity, and future directions.

Automatic Machine Learning?

SciPy 2016 contributed talk

In this talk I'm discussing the why and how of automatic machine learning. I start with an explanation of the goals of automatic machine learning, and introduce meta-learning. The talk goes on to discuss recent research, available implementation and what I think we should be working on in this area.

Machine Learning with Scikit Learn 2016

SciPy 2016 Tutorial with Sebastian Raschka

The two times four hours of introductory tutorial from 2016 SciPy. Starting from what is machine learning to model building and more advanced topics. This is a somewhat updated version from last year's tutorial.

The session is split up into two videos.

Machine Learning with Scikit Learn

SciPy 2015 Tutorial with Kyle Kastner

The two times four hours of introductory tutorial from 2015 SciPy. Starting from what is machine learning to model building and more advanced topics. The material is loosely based on the 2013 SciPy tutorial by Olivier Grisel, Gael Varoquaux and Jake Vanderplas.

The session is split up into two videos.

Machine Learning with Scikit Learn (short)

ODSC West 2015 Introduction to scikit-learn (90min)

This talk introduction covers data representation, basic API for supervised and unsupervised learning, cross-validation, grid-search, pipelines, text processing and details about some of the most popular machine learning models. The talk concludes with remarks on scaling up computation to large datasets, and how to perform out-of-core learning with scikit-learn.

Slides Notebooks

Large-Scale Non-Linear Learning on a single CPU

PyGotham 2015 contributed talk

In the days of the "big data" buzz, many people build data driven applications on clusters from the start. However, working with distributed computing is not only pricey, but also requires a large engineering effort and removes interactivity from the data exploration process. In this talk I will demonstrate how to learn powerful nonlinear models on a single machine, even with large data sets. This can be achieved using the partial_fit interface provided by scikit-learn, that implements stochastic updates. Together with stateless transformation of the data, such as hashing, kernel approximation and random projections, these allow incrementally building a model without the need to store all the data in memory, or even on disk.

Slides

News from Scikit-Learn 0.16 and Soon-To-Be Gems for the Next Release

O'Reilly Webcast, live April 02 / 2015

Olivier Grisel and I give an overview of the 0.16 release, moderated by Ben Lorica. We also show off a couple of cool feature in the PRs, and there is a Q&A with the viewers at the end.

You need to register to see the video, but it is free.

Advanced Scikit-Learn v3

PyData Amsterdam 2016

A somewhat expanded version of my PyData NYC talk "Advanced scikit-learn", the talk briefly explains the scikit-learn API and goes into some depth on pipelining and grid-searches. I describe the advantages of randomized parameter search, when it is applicable, and which metrics to use for model selection. At the end, I talk about how to do out-of-core learning.

Introduction to Scikit-Learn

Strata San Jose 2015

This tutorial covers basic concepts of machine learning, such as supervised and unsupervised learning, cross validation and model selection. I talk about how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline. I also go in-depth on a couple of algorithms and describe what overfitting and underfitting looks like for these.

Advanced Scikit-Learn

PyData NYC 2014

A brief intoduction into scikit-learn, and the basics of pipelining and grid searches. I talk about the bias-variance tradeoff, and helper functions in scikit-learn to diagnose over-fitting and under-fitting. I also talk about randomized hyperparameter search and out-of-core learning. The last part of the talk is a brief introduction to PyStruct and Structured Prediction in general.

Keynote: Commodity Machine Learning

PyData Berlin 2014

I talk about recent developments in commoditizing machine learning, and what I think needs to be done to help non-experts to apply machine-learning more easily and more effectively.