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For years, Introduction to Statistical Learning with Applications in R, higher often called ISLR, has been cherished—by each machine studying newcomers and practitioners alike—as among the finest machine studying textbooks.
Now that the Python version of the e-book, Introduction to Statistical Learning with Applications in Python—or ISL with Python—is right here, the group is all of the extra excited!
Glad you requested. 😀
In case you’ve been within the machine studying house for some time, chances are high you’ve already heard, learn, or used the R model of the e-book earlier than. And what you appreciated greatest about it. However right here’s my story.
The summer time earlier than I began grad college, I made a decision to show myself machine studying. I used to be fortunate to stumble throughout ISLR early in my machine studying journey. The authors of ISLR do an incredible job at breaking down complicated machine studying algorithms in an easy-to-follow method—together with the required mathematical foundations—with out overwhelming the learners. That is a side of the e-book I loved.
The code examples and labs in ISLR, nevertheless, are in R. Sadly sufficient, I didn’t know R again then, however was snug programming in Python. So I had two choices.
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I might train myself R. Or I might use different sources—tutorials and documentation—to construct fashions in Python. Like most different Pythonistas, I selected the second choice (yeah, the extra acquainted route, I do know).
Whereas R is nice for statistical evaluation, Python is an efficient first language in case you’re simply beginning out in your knowledge journey.
However this isn’t an issue anymore! As a result of this new Python version enables you to code alongside and construct machine studying fashions in Python. No extra worries about having to choose up a brand new programming language to observe alongside.
Story time’s up! Let’s take a more in-depth have a look at the contents of the e-book.
When it comes to the content material, the Python version is fairly just like the R version. Nonetheless, it is an acceptable adaptation for Python which is anticipated. This e-book additionally features a Python programming crash course part to study the fundamentals.
This e-book covers ample breadth. From foundations of statistical studying, supervised and unsupervised studying algorithms to deep studying and extra, the e-book is organized into the next chapters:
- Statistical studying
- Linear regression
- Resampling strategies
- Linear mannequin choice and regularization
- Shifting past linearity
- Tree-based strategies
- Assist Vector Machines
- Deep Studying (covers vanilla neural networks to ConvNets and recurrent neural networks)
- Survival Evaluation and Censored Information
- Unsupervised studying
- A number of testing (a deep dive into speculation testing)
The e-book makes use of datasets sourced from publicly out there repositories such because the UCI Machine Studying repository and different comparable sources. Some examples embody datasets on bike sharing, bank card default, fund administration, and crime charges.
Studying to gather knowledge from varied sources by means of the method of net scraping, and importing knowledge from sources are tremendous essential for a knowledge science challenge.
Nonetheless for a learner who’s unfamiliar with the info assortment step, it will probably introduce friction within the studying course of in the event that they wish to use the e-book to get the hold of each the speculation and hands-on sections.
To facilitate a easy studying expertise, the e-book comes with an accompanying ISLP bundle:
- The ISLP bundle is on the market for all main platforms: Linux, Home windows, and MacOS.
- You possibly can set up ISLP utilizing pip:
pip set up islpideally in a digital atmosphere in your machine.
The ISLP bundle has a comprehensive documentation. The ISLP bundle comes with knowledge loading utilities. If you work with a specific dataset, the docs web page provides you ready-to-access info on the varied options within the dataset, the variety of information, and starter code to load the info right into a pandas dataframe.
It additionally has helper capabilities and performance to create higher-order options like polynomial and spline options.
Producing polynomial options | Image from ISLP docs
For a extra full studying expertise, you possibly can learn within the knowledge from their sources, carry out characteristic engineering with out utilizing the ISLP bundle.
If you’re constructing fashions, you possibly can attempt scikit-learn-only implementation and PyTorch or Keras for the deep studying sections.
Information Science and Machine Studying Novices: If you’re a newbie who prefers a self-taught path to study machine studying, this e-book is a good studying useful resource.
ML Practitioners: As a machine studying practitioner, you’ll have expertise constructing machine studying fashions. However going again to the fundamentals corresponding to speculation testing and different algorithms will be useful.
Educators: The speculation and the labs collectively make this e-book an incredible companion for a primary course in machine studying. Most universities and knowledge science bootcamps as of late train machine studying. So in case you are an educator who’s educating or trying to train a machine studying course, this can be a nice course textbook to think about.
And that is a wrap. Introduction to Statistical Studying with Python has been probably the most thrilling releases of this summer time.
You possibly can head over to statlearning.com and begin studying the Python version. Whereas the gentle copy is free to learn, the paperback on Amazon bought out on the very first day. So we’re excited to see you profit from the e-book. Begin studying it right now. Blissful studying!
Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, knowledge science, and content material creation. Her areas of curiosity and experience embody DevOps, knowledge science, and pure language processing. She enjoys studying, writing, coding, and low! Presently, she’s engaged on studying and sharing her information with the developer group by authoring tutorials, how-to guides, opinion items, and extra.