Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Beginning Data Science with Python and Jupyter
  • Table Of Contents Toc
Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

By : Galea
4 (5)
Buy this Book
close
close
Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

4 (5)
By: Galea
Buy this Book

Overview of this book

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
Table of Contents (5 chapters)
close
close
close
Preface
Icon
Preface
Icon
What This Book Covers
Icon
What You Need for This Book
Icon
Who This Book is for
Icon
Conventions
Icon
Reader Feedback
Icon
Customer Support
Icon
Questions
Lock Free Chapter
1
1. Jupyter Fundamentals
Icon
1. Jupyter Fundamentals
Icon
Lesson Objectives
Icon
Basic Functionality and Features
Icon
Our First Analysis - The Boston Housing Dataset
Icon
Summary
2
2. Data Cleaning and Advanced Machine Learning
Icon
2. Data Cleaning and Advanced Machine Learning
Icon
Preparing to Train a Predictive Model
Icon
Training Classification Models
Icon
Summary
3
3. Web Scraping and Interactive Visualizations
chevron up
Icon
3. Web Scraping and Interactive Visualizations
Icon
Lesson Objectives
Icon
Scraping Web Page Data
Icon
Interactive Visualizations
Icon
Summary
4
Index
Icon
Index

Training Classification Models

As we've already seen in the previous lesson, using libraries such as scikit-learn and platforms such as Jupyter, predictive models can be trained in just a few lines of code. This is possible by abstracting away the difficult computations involved with optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden instead. With this simplicity also comes the danger of misusing algorithms, for example, by overfitting during training or failing to properly test on unseen data. We'll show how to avoid these pitfalls while training classification models and produce trustworthy results with the use of k-fold cross validation and validation curves.

Subtopic A: Introduction to Classification Algorithms

Recall the two types of supervised machine learning: regression and classification. In regression, we predict a continuous target variable. For example, recall the linear and polynomial models from the first lesson. In...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Beginning Data Science with Python and Jupyter
End of Section 3
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon