Intro to Machine Learning Class

Detailed Course Description

Before the course

• Installation - You will be given detailed instructions on how to install python onto your machine and set up an environment to run all the code during class.
• Juptyer Notebooks - We will be using the excellent (though still needing improvement) Jupyter Notebook to run most of the code during class. They provide an interactive coding environment to quickly execute code, get feedback, and make notes.
• Assignment - A short assignment introducing how to use Jupyter Notebooks is expected to be completed. You will also learn how to add and edit notes using Markdown.
• Private Slack Channel - Upon registration, you be given access to Ted's private slack channel where you can directly communicate him and ask him questions about the course.
• Master Machine Learning with Python - You will get a digital copy (complete with a PDF and Jupyter Notebooks) of a sample

Day 1

• The machine learning model - Models are anything that help us represent the real world. You will learn how machine learning models are a subset of mathematical models that learn from data.
• The scikit-learn estimator - In this class, we will use the excellent scikit-learn library to handle all of our machine learning. scikit-learn uses a generic class of objects called estimators that do all of the machine learning as well as other tasks that learn from data. You will learn how to use an estimator with the three step process - import, instantiate, fit.
• Linear regression - You will learn how to model data with linear regression, a simple and practical model that is useful starting point before diving into more advanced models. You will learn how the parameters of the model are fit and understand what it means to minimize squared error.
• Model evaluation and selection - The goal of machine learning is to build a model that is accurate on data that it has yet to encounter. You will learn how to determine how well a model is likely to perform on unseen data by properly evaluating it with techniques such as cross validation. You will also learn how to choose 'better' models by choosing combinations of hyper-parameter values that yield good cross-validated results.
• Transformers and pipelines - We take a step back to learn how to transform our data before machine learning so that we can build a better model. You will learn about different transformations such as filling in missing values and standardizing features. You will then learn how to combine multiple transformations together in a pipeline.
• The ColumnTransformer - With the release of scikit-learn version 0.20, the ColumnTransformer was made available to give us a much easier and direct way to applying different transformations to different features of our data. This greatly simplified the workflow and allowed us to build a single object containing all the transformation and machine learning steps.
• Model persistence - Building a model is great fun, but we need a way to access it in the future. You will learn how to save a model as a file and retrieve it any time you need to make a new prediction.

After the course 

• Certificate of completion - Upon conclusion of the course, you will be given a direct path towards mastering the fundamentals of machine learning using python. If you go on to complete all the tasks on this path, you will receive a certificate of completion.
• Continual access to material - You will always have access to the material after the course has completed. Ted upgrades and adds to his material on a regular basis. You will always have immediate access to the latest updates.
• Continual Slack Access - You will also have access to Slack forever meaning you can interact with Ted along with all of the previous students.