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Beginning Data Science with Python and Jupyter
Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter: Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data

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Beginning Data Science with Python and Jupyter

Chapter 2. Data Cleaning and Advanced Machine Learning

The goal of data analytics in general is to uncover actionable insights that result in positive business outcomes. In the case of predictive analytics, the aim is to do this by determining the most likely future outcome of a target, based on previous trends and patterns.

The benefits of predictive analytics are not restricted to big technology companies. Any business can find ways to benefit from machine learning, given the right data.

Companies all around the world are collecting massive amounts of data and using predictive analytics to cut costs and increase profits. Some of the most prevalent examples of this are from the technology giants Google, Facebook, and Amazon, who utilize big data on a huge scale. For example, Google and Facebook serve you personalized ads based on predictive algorithms that guess what you are most likely to click on. Similarly, Amazon recommends personalized products that you are most likely to...

Preparing to Train a Predictive Model

Here, we will cover the preparation required to train a predictive model. Although not as technically glamorous as training the models themselves, this step should not be taken lightly. It's very important to ensure you have a good plan before proceeding with the details of building and training a reliable model. Furthermore, once you've decided on the right plan, there are technical steps in preparing the data for modeling that should not be overlooked.

Note

We must be careful not to go so deep into the weeds of technical tasks that we lose sight of the goal.

Technical tasks include things that require programming skills, for example, constructing visualizations, querying databases, and validating predictive models. It's easy to spend hours trying to implement a specific feature or get the plots looking just right. Doing this sort of thing is certainly beneficial to our programming skills, but we should not forget to ask ourselves if it...

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...

Summary

In this lesson, we have seen how predictive models can be trained in Jupyter Notebooks.

To begin with, we talked about how to plan a machine learning strategy. We thought about how to design a plan that can lead to actionable business insights and stressed the importance of using the data to help set realistic business goals. We also explained machine learning terminology such as supervised learning, unsupervised learning, classification, and regression.

Next, we discussed methods for preprocessing data using scikit-learn and pandas. This included lengthy discussions and examples of a surprisingly time-consuming part of machine learning: dealing with missing data.

In the latter half of the lesson, we trained predictive classification models for our binary problem, comparing how decision boundaries are drawn for various models such as the SVM, k-Nearest Neighbors, and Random Forest. We then showed how validation curves can be used to make good parameter choices and how dimensionality...

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Key benefits

  • Get up and running with the Jupyter ecosystem and some example datasets
  • Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests
  • Discover how you can use web scraping to gather and parse your own bespoke datasets

Description

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.

Who is this book for?

This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

What you will learn

  • Get up and running with the Jupyter ecosystem and some example datasets
  • Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests
  • Plan a machine learning classification strategy and train classification, models
  • Use validation curves and dimensionality reduction to tune and enhance your models
  • Discover how you can use web scraping to gather and parse your own bespoke datasets
  • Scrape tabular data from web pages and transform them into Pandas DataFrames
  • Create interactive, web-friendly visualizations to clearly communicate your findings
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Publication date : Jun 05, 2018
Length: 194 pages
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Language : English
ISBN-13 : 9781789532029
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Language : English
ISBN-13 : 9781789532029
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Table of Contents

4 Chapters
1. Jupyter Fundamentals Chevron down icon Chevron up icon
2. Data Cleaning and Advanced Machine Learning Chevron down icon Chevron up icon
3. Web Scraping and Interactive Visualizations Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(5 Ratings)
5 star 60%
4 star 0%
3 star 20%
2 star 20%
1 star 0%
Andrew Aug 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are lots of great Python tutorials out there, and as somebody who has already worked through ‘Automate the Boring Stuff with Python’ I wanted a book to show me how to get started with data science without going over all of the basics of the language again. This book is ideal for that. It shows you step-by-step how to get up and running with Jupyter, and then works through Python data science libraries that I’d heard of before but never actually used. The whole thing feels like it was written for a classroom environment as it’s got lots of step-by-step example and activities, which works quite well given the subject matter.
Amazon Verified review Amazon
Gabriel Abdulai Nov 02, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book was in an excellent shape, like new.I definitely recommend this seller.
Amazon Verified review Amazon
Poet1103 Sep 29, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book packs a lot into a small space! You will need some basic knowledge of python to benefit from all examples/labs.
Amazon Verified review Amazon
The Reviewer Jan 04, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Used this book for a college course. YouTube is better. At least it's reasonably priced. Okay for a beginner.
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Richard E. West May 22, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I'm about 20% in and find this book poorly organized and concepts only marginally explained. This could be a case of an author knowing the subject too well and forgetting how to explain it to others.
Amazon Verified review Amazon
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