Search icon CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from  Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Pages 384 pages
Edition 1st Edition
Languages
Author (1):
Tarek Amr Tarek Amr
Profile icon Tarek Amr
Toc

Table of Contents (18) Chapters close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy

Introduction to scikit-learn

Since you have already picked up this book, you probably don't need me to convince you why machine learning is important. However, you may still have doubts about why to use scikit-learn in particular. You may encounter names such as TensorFlow, PyTorch, and Spark more often during your daily news consumption than scikit-learn. So, let me convince you of my preference for the latter.

It plays well with the Python data ecosystem

scikit-learn is a Python toolkit built on top of NumPy, SciPy, and Matplotlib. These choices mean that it fits well into your daily data pipeline. As a data scientist, Python is most likely your language of choice since it is good for both offline analysis and real-time implementations. You will also be using tools such as pandas to load data from your database, which allows you to perform a vast amount of transformation to your data. Since both pandas and scikit-learn are built on top of NumPy, they play...

You have been reading a chapter from
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Published in: Jul 2020 Publisher: Packt ISBN-13: 9781838826048
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime