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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

By : Tarek Amr

4.8 (4)

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

4.8 (4)

By: Tarek Amr

Overview of this book

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: Supervised Learning

Section 1: Supervised Learning

Free Chapter

Introduction to Machine Learning

Introduction to Machine Learning

Understanding machine learning

The model development life cycle

Introduction to scikit-learn

Installing the packages you need

Summary

Further reading

Making Decisions with Trees

Making Decisions with Trees

Understanding decision trees

How do decision trees learn?

Getting a more reliable score

Tuning the hyperparameters for higher accuracy

Visualizing the tree's decision boundaries

Building decision tree regressors

Summary

Making Decisions with Linear Equations

Making Decisions with Linear Equations

Understanding linear models

Predicting house prices in Boston

Regularizing the regressor

Finding regression intervals

Getting to know additional linear regressors

Using logistic regression for classification

Getting to know additional linear classifiers

Summary

Preparing Your Data

Preparing Your Data

Imputing missing values

Encoding non-numerical columns

Homogenizing the columns' scale

Selecting the most useful features

Summary

Image Processing with Nearest Neighbors

Image Processing with Nearest Neighbors

Nearest neighbors

Loading and displaying images

Image classification

Using custom distances

Using nearest neighbors for regression

More neighborhood algorithms

Reducing the dimensions of our image data

Summary

Classifying Text Using Naive Bayes

Classifying Text Using Naive Bayes

Splitting sentences into tokens

Vectorizing text into matrices

Understanding Naive Bayes

Classifying text using a Naive Bayes classifier

Creating a custom transformer

Summary

Section 2: Advanced Supervised Learning

Section 2: Advanced Supervised Learning

Neural Networks – Here Comes Deep Learning

Neural Networks – Here Comes Deep Learning

Getting to know MLP

Classifying items of clothing

Untangling the convolutions

MLP regressors

Summary

Ensembles – When One Model Is Not Enough

Ensembles – When One Model Is Not Enough

Answering the question why ensembles?

Downloading the UCI Automobile dataset

Using random forest for regression

Using random forest for classification

Using bagging regressors

Using gradient boosting to predict automobile prices

Using AdaBoost ensembles

Exploring more ensembles

Summary

The Y is as Important as the X

The Y is as Important as the X

Scaling your regression targets

Estimating multiple regression targets

Dealing with compound classification targets

Calibrating a classifier's probabilities

Calculating the precision at k

Summary

Imbalanced Learning – Not Even 1% Win the Lottery

Imbalanced Learning – Not Even 1% Win the Lottery

Getting the click prediction dataset

Installing the imbalanced-learn library

Predicting the CTR

Sampling the training data

Equal opportunity score

Summary

Section 3: Unsupervised Learning and More

Section 3: Unsupervised Learning and More

Clustering – Making Sense of Unlabeled Data

Clustering – Making Sense of Unlabeled Data

Understanding clustering

K-means clustering

Agglomerative clustering

DBSCAN

Summary

Anomaly Detection – Finding Outliers in Data

Anomaly Detection – Finding Outliers in Data

Unlabeled anomaly detection

Detecting anomalies using basic statistics

Detecting outliers using EllipticEnvelope

Outlier and novelty detection using LOF

Detecting outliers using isolation forest

Summary

Recommender System – Getting to Know Their Taste

Recommender System – Getting to Know Their Taste

The different recommendation paradigms

Downloading surprise and the dataset

Using KNN-inspired algorithms

Using baseline algorithms

Using singular value decomposition

Deploying machine learning models in production

Summary

Other Books You May Enjoy

Other Books You May Enjoy

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How do decision trees learn?

It's time to find out how decision trees actually learn in order to configure them. In the internal structure we just printed, the tree decided to use a petal width of 0.8 as its initial splitting decision. This was done because decision trees try to build the smallest possible tree using the following technique.

It went through all the features trying to find a feature (petal width, here) and a value within that feature (0.8, here) so that if we split all our training data into two parts (one for petal width ≤ 0.8, and one for petal width > 0.8), we get the purest split possible. In other words, it tries to find a condition where we can separate our classes as much as possible. Then, for each side, it iteratively tries to split the data further using the same technique.

Splitting criteria

If we onlyhad two classes, an ideal split would put members of one class on one side and members of the others on the other...

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