This is the code repository for Machine Learning with scikit-learn Quick Start Guide, published by Packt.
Classification, regression, and clustering techniques in Python
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
This book covers the following exciting features:
- Learn how to work with all scikit-learn's machine learning algorithms
- Install and set up scikit-learn to build your first machine learning model
- Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
- Perform classification and regression machine learning
- Use an effective pipeline to build a machine learning project from scratch
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
from sklearn.naive_bayes import GaussianNB
#Initializing an NB classifier
nb_classifier = GaussianNB()
#Fitting the classifier into the training data
nb_classifier.fit(X_train, y_train)
Following is what you need for this book: This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Pandas (= 0.23.4) Matplotlib (= 3.0.0) Scikit-learn (= 0.20.0) Tree (Latest) NumPy (= 1.15.1 Pydotplus (= 2.0.2) Image (= 5.3.0) | Windows, Mac OS X |
| 2 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) | Windows, Mac OS X |
| 3 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) | Windows, Mac OS X |
| 4 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) | Windows, Mac OS X |
| 5 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) | Windows, Mac OS X |
| 6 | Pandas (0.23.4) Scikit-learn (= 0.20.0) Tree (Latest) NumPy (= 1.15.1) ydotplus (= 2.0.2) IL (= 5.3.0) | Windows, Mac OS X |
| 7 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) | Windows, Mac OS X |
| 8 | Pandas (= 0.23.4) Scikit-learn (= 0.20.0) Scikit-plot (= 0.3.7) | Windows, Mac OS X |
Click on the following link to see the Code in Action:
Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built.
Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.
Hands-On Data Visualization with Bokeh
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