PyData Chicago 2016 This tutorial provides you with a comprehensive introduction to machine learning in Python using the popular scikit-learn library. We will learn how to tackle common problems in predictive modeling and clustering analysis that can be used in real-world problems, in business and in research applications. And we will implement certain algorithms as scratch as well, to internalize the inner workings This tutorial will teach you the basics of scikit-learn. We will learn how to leverage powerful algorithms from the two main domains of machine learning: supervised and unsupervised learning. In this talk, I will give you a brief overview of the basic concepts of classification and regression analysis, how to build powerful predictive models from labeled data.
Hi there, my name is Harrison and I frequently do Python programming tutorials on PythonProgramming.net and YouTube.com/sentdex. I do my best to produce tutorials for beginner-intermediate programmers, mainly by making sure nothing is left to abstraction and hand waving. The most recent series is an in-depth machine learning course, aimed at breaking down the complex ML concepts that are typically just "done for you" in a hand-wavy fashion with packages and modules. The machine learning series is aimed at just about anyone with a basic understanding of Python programming and the willingness to learn. If you're confused about something we're doing, I can either help, or point you towards a tutorial that I've done already (I have about 1,000) to help.
Then read Michael J. Garbade's Understanding K-means Clustering in Machine Learning and implement k-means for yourself. Then take a look at Gabriel Pierobon's DBSCAN clustering for data shapes k-means can't handle well (in Python) to implement a density-based clustering model. Now that we have sampled around, let's switch gears back to classification and check out a more complex algorithm.