Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

ML Pipeline for Modeling and Prediction

In the last section, we introduced the concept of the estimator, which chains together different transformation processes to make feature extraction easier. We also saw the demonstration of the fit and transform functions for the estimator we built. Estimators have far more capabilities than just fit and transform. Estimators can also be used to chain together classifiers such as logistic regression, KNN, or random forest classifiers along with the transformation steps.

When classifiers are introduced into the estimator, the estimator also inherits many of the functions of the classifiers, such as scoring and predicting.

So, now we have a single engine that is capable of performing very diverse functions that otherwise would have to be performed by separate functions. Here lies the beauty of the pipeline utility, which enables us to build all-encompassing functions in a single engine.

In the next exercise, we will demonstrate the...