Book Image

Python: Real-World Data Science

By : Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka
Book Image

Python: Real-World Data Science

By: Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka

Overview of this book

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Table of Contents (12 chapters)
Free Chapter
Table of Contents
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline

Chapter 2. Classifying with scikit-learn Estimators

The scikit-learn library is a collection of data mining algorithms, written in Python and using a common programming interface. This allows users to easily try different algorithms as well as utilize standard tools for doing effective testing and parameter searching. There are a large number of algorithms and utilities in scikit-learn.

In this chapter, we focus on setting up a good framework for running data mining procedures. This will be used in later chapters, which are all focused on applications and techniques to use in those situations.

The key concepts introduced in this chapter are as follows:

  • Estimators: This is to perform classification, clustering, and regression
  • Transformers: This is to perform preprocessing and data alterations
  • Pipelines: This is to put together your workflow into a replicable format

scikit-learn estimators

Estimators are scikit-learn's abstraction, allowing for the standardized implementation of a...