Book Image

Supervised Machine Learning with Python

By : Taylor Smith
Book Image

Supervised Machine Learning with Python

By: Taylor Smith

Overview of this book

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.
Table of Contents (11 chapters)
Title Page
Copyright and Credits
About Packt

Setting up the environment

We will go ahead and get our environment set up. Now that we have walked through the preceding example, let's go ahead and get our Anaconda environment set up. Among other things, Anaconda is a dependency management tool that will allow us to control specific versioning of each of the packages that we want to use. We will go to the Anaconda website through this link,, and click on the Download tab.


The package that we're building is not going to work with Python 2.7. So, once you have Anaconda, we will perform a live coding example of an actual package setup, as well as the environment setup that's included in the .yml file that we built.

Once you have Anaconda set up inside the home directory, we are going to use the environment.yml file. You can see that the name of the environment we're going to create is packt-sml for supervised machine learning. We will need NumPy, SciPy, scikit-learn, and pandas. These are all scientific computing and data analysis libraries. Matplotlib is what we were using to plot those plots inside the Jupyter Notebook, so you're going to need all those plots. The conda package makes it really easy to build this environment. All we have to do is type conda env create and then -f to point it to the file, go to Hands-on-Supervised-Machine-Learning-with-Python-master, and we're going to use the environment.yml as shown in the following command:

cat environment.yml
conda env create -f environment.yml

As this is the first time you're creating this, it will create a large script that will download everything you need. Once you have created your environment, you need to activate it. So, on a macOS or a Linux machine, we will type source activate packt-sml.



If you're on Windows, simply type activate packt-sml, which will activate that environment:

 source activate packt-sml

The output is as follows:

In order to build the package, we will type the cat command. We can inspect this quickly:


Take a look at this Basically, this is just using setup tools to install the package. In the following screenshot, we see all the different sub models:

We will build the package by typing the python install command. Now, when we go into Python and try to import packtml, we get the following output:

In this section, we have installed the environment and built the package. In the next section, we will start covering some of the theory behind supervised machine learning.