Book Image

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

By : Tarek Amr
Book Image

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

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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Supervised Learning
Section 1: Supervised Learning
Free Chapter
2
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
3
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
4
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
5
Preparing Your Data
Preparing Your Data
Imputing missing values
Encoding non-numerical columns
Homogenizing the columns' scale
Selecting the most useful features
Summary
6
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
7
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
8
Section 2: Advanced Supervised Learning
Section 2: Advanced Supervised Learning
9
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
10
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
11
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
12
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
13
Section 3: Unsupervised Learning and More
Section 3: Unsupervised Learning and More
14
Clustering – Making Sense of Unlabeled Data
Clustering – Making Sense of Unlabeled Data
Understanding clustering
K-means clustering
Agglomerative clustering
DBSCAN
Summary
15
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
16
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
17
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DBSCAN

"You never really understand a person until you consider things from his point of view."
- Harper Lee

The acronym DBSCAN stands for density-based spatial clustering of applications with noise. It sees clusters as areas of high density separated by areas of low density. This allows it to deal with clusters of any shape. This is in contrast to the K-means algorithm, which assumes clusters to be convex; that is, data blobs with centroids. The DBSCANalgorithm starts by identifying the core samples. These are points that have at least min_samples around them within a distance of eps (ε). Initially, a cluster is built out of its core samples. Once a core sample has been identified, its neighbors are also examined and added to the cluster if they meet the core sample criteria. Then, the cluster is expanded so that we can add non-core samples to it. These are samples that can be reached directly from the core samples within a distance...

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