Tag Archives: dataset

Scikit-Learn Cookbook

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Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB

Scikit-Learn Cookbook
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
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Scikit-Learn Cookbook

FREEDownload : Scikit-Learn Cookbook

Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB

Scikit-Learn Cookbook
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
Download links
Buy Premium To Support Me & Get Resumable Support & Fastest Speed!

Continue reading

Scikit-Learn Cookbook (PDF)

FREEDownload : Scikit-Learn Cookbook (PDF)

Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

Scikit-Learn Cookbook (PDF)
About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.

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Agile Data Science: Building Data Analytics Applications with Hadoop

Russell Jurney, "Agile Data Science: Building Data Analytics Applications with Hadoop"
English | ISBN: 1449326269 | 2013 | 178 pages | PDF, EPUB | 24 MB
Mining big data requires a deep investment in people and time. How can you be sure youre building the right models? With this hands-on book, youll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.

Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. Youll learn an iterative approach that enables you to quickly change the kind of analysis youre doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.
Create analytics applications by using the agile big data development methodology
Build value from your data in a series of agile sprints, using the data-value stack
Gain insight by using several data structures to extract multiple features from a single dataset
Visualize data with charts, and expose different aspects through interactive reports
Use historical data to predict the future, and translate predictions into action
Get feedback from users after each sprint to keep your project on track

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