Machine Learning With Python
When we talk about Data Science and the Data Science Pipeline, we are typically referring to the management of data flows for a specific purpose - the modeling of some hypothesis. The models that we construct can then be used in data products as an engine to create more data and actionable results.
Machine learning is the art of training some model by using existing data along with a statistical method to create a parametric representation of a model that fits the data. In other words, a machine learning algorithm uses statistical processes to learn from examples and then applies what it has learned to future inputs to predict an outcome.
Machine learning can classically be summarized with two methodologies: supervised and unsupervised learning.
In supervised learning, the “correct answers” are annotated ahead of time and the algorithm tries to fit a decision space based on those answers.
In unsupervised learning, algorithms try to group like examples together, inferring similarities usually via distance metrics.
These learning types allow us to explore data and categorize them in a meaningful way, predicting where new data will fit into our models.
What You Will Learn
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientist's toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology. For more on Scikit-Learn see: Six Reasons why I recommend Scikit-Learn (O’Reilly Radar).
This course will cover the following topics:
An introduction to machine learning
Loading datasets into Scikit-Learn
Building models and model persistence
Feature extraction from data sets
Model selection and evaluation
Building a data pipeline
After this course you should understand the basics of machine learning and how to implement machine learning algorithms on your data sets using Python and Scikit-Learn. In particularly you should understand basic regressions, classifiers, and clustering algorithms and how to fit a model and use it to predict future outcomes.
You must be familiar with Python before participating in this course, and have familiarity with the command line. You must also have all software installed and ready for your particular operating system. Ensure that you perform the following tasks and are familiar with the concepts at the following links.