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)

Summary

In this chapter, we were introduced to ML pipelines. Through the myriad of exercises that we implemented, we realized that to squeeze out the best performance from our ML models, we must try various permutations and combinations of features, models, and model parameters. Finding the right combination is indeed a time-consuming process. Using an ML pipeline is a technique that automates this process and spares us a lot of manual experimentation.

Within this chapter, we progressively implemented different parts of an ML workflow. We created a processing engine and added dimensionality reduction and modeling to the processing engine. Later on, we did spot-checking with various models and also performed grid search to find the best parameters. In the process, we identified how ML pipelines made all these processes simpler.

The objective of this chapter was to enable you to carry out different experiments on your ML workflow using a very powerful toolset. It is left to the...