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Machine Learning with Python Cookbook: Recipes for Real-World Applications

Charles Dickens

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Published in Machine Learning With Python Cookbook: Practical Solutions From Preprocessing To Deep Learning

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Machine learning is a powerful tool that allows computers to learn from data without being explicitly programmed. This makes it ideal for a wide range of tasks, from predicting customer churn to detecting fraud.

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

by Chris Albon

4.6 out of 5

Language	:	English
File size	:	4797 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	368 pages

Python is a popular programming language for machine learning because it is easy to learn and use, and there are a wide range of libraries and frameworks available for machine learning tasks.

This cookbook provides a collection of recipes that will help you master machine learning with Python. The recipes cover a wide range of topics, from basic data preprocessing to advanced deep learning techniques.

What You'll Learn

This cookbook will teach you how to:

* Preprocess data for machine learning * Train and evaluate machine learning models * Use machine learning models to make predictions * Deploy machine learning models to production

Who This Book Is For

This book is for anyone who wants to learn how to use machine learning with Python. Whether you're a beginner or an experienced data scientist, you'll find something in this book to help you.

* Chapter 1: Data Preprocessing * Chapter 2: Linear Regression * Chapter 3: Logistic Regression * Chapter 4: Decision Trees * Chapter 5: Random Forests * Chapter 6: Support Vector Machines * Chapter 7: Naive Bayes * Chapter 8: Clustering * Chapter 9: Dimensionality Reduction * Chapter 10: Deep Learning

Chapter 1: Data Preprocessing

The first step in any machine learning project is to preprocess the data. This involves cleaning the data, removing outliers, and transforming the data into a format that is suitable for machine learning algorithms.

This chapter provides recipes for the following data preprocessing tasks:

* Cleaning data * Removing outliers * Transforming data * Scaling data * Normalizing data

Chapter 2: Linear Regression

Linear regression is a simple but powerful machine learning algorithm that can be used to predict continuous values. This chapter provides recipes for the following linear regression tasks:

* Fitting a linear regression model * Evaluating a linear regression model * Using a linear regression model to make predictions

Chapter 3: Logistic Regression

Logistic regression is a machine learning algorithm that can be used to predict binary outcomes. This chapter provides recipes for the following logistic regression tasks:

* Fitting a logistic regression model * Evaluating a logistic regression model * Using a logistic regression model to make predictions

Chapter 4: Decision Trees

Decision trees are a machine learning algorithm that can be used to predict both continuous and binary outcomes. This chapter provides recipes for the following decision tree tasks:

* Fitting a decision tree model * Evaluating a decision tree model * Using a decision tree model to make predictions

Chapter 5: Random Forests

Random forests are a machine learning algorithm that combines multiple decision trees to improve accuracy. This chapter provides recipes for the following random forest tasks:

* Fitting a random forest model * Evaluating a random forest model * Using a random forest model to make predictions

Chapter 6: Support Vector Machines

Support vector machines are a machine learning algorithm that can be used to predict both continuous and binary outcomes. This chapter provides recipes for the following support vector machine tasks:

* Fitting a support vector machine model * Evaluating a support vector machine model * Using a support vector machine model to make predictions

Chapter 7: Naive Bayes

Naive Bayes is a machine learning algorithm that can be used to predict both continuous and binary outcomes. This chapter provides recipes for the following Naive Bayes tasks:

* Fitting a Naive Bayes model * Evaluating a Naive Bayes model * Using a Naive Bayes model to make predictions

Chapter 8: Clustering

Clustering is a machine learning technique that can be used to group data points into similar groups. This chapter provides recipes for the following clustering tasks:

* K-means clustering * Hierarchical clustering * Density-based spatial clustering

Chapter 9: Dimensionality Reduction

Dimensionality reduction is a machine learning technique that can be used to reduce the number of features in a dataset. This chapter provides recipes for the following dimensionality reduction tasks:

* Principal component analysis * Linear discriminant analysis * Singular value decomposition

Chapter 10: Deep Learning

Deep learning is a machine learning technique that uses artificial neural networks to learn from data. This chapter provides recipes for the following deep learning tasks:

* Convolutional neural networks * Recurrent neural networks * Autoencoders

This cookbook provides a comprehensive collection of recipes that will help you master machine learning with Python. The recipes cover a wide range of topics, from basic data preprocessing to advanced deep learning techniques.

Whether you're a beginner or an experienced data scientist, you'll find something in this book to help you.

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

by Chris Albon

4.6 out of 5