Introduction to scikit learn machine learning practice

# Good books recommend ## Good book adventure season #scikit-learn Introduction to machine learning 《scikit-learn Machine learning practice 》, Jingdong Dangdang and tmall are on sale . Two color printing , pricing 69 element , It's cheaper to give a discount . Start with algorithms and cases , Quickly master machine learning

Background of the book

scikit-learn The project was first developed by data scientists David Cournapeau stay 2007 Year launch , need NumPy and SciPy Other package support , It is Python Language for machine learning applications and the development of an open-source framework .

Machine learning is an interdisciplinary subject , Probability theory 、 statistical 、 Approximation theory 、 Convex analysis 、 Algorithm complexity theory and other disciplines . It specializes in how computers simulate or implement human learning behavior , To acquire new knowledge or skills , Reorganize the existing knowledge structure and make it continuously improve its performance . It's the core of AI , Even if the computer has the fundamental way of intelligence .

This book aims at the field of machine learning , Describes a variety of learning models 、 Strategy 、 Algorithm 、 Theory and Application , be based on Python3 Use scikit-learn The toolkit demonstrates the process of algorithm solving practical problems . Readers interested in machine learning can get started quickly through this book , Quickly qualified for machine learning positions , Become a talent in the era of artificial intelligence .

The content of this book

This book is divided into 13 Chapter , Explain the typical algorithm of machine learning systematically , The content includes an overview of machine learning 、 Data feature extraction 、scikit-learn Estimator classification 、 naive bayesian classification 、 Linear regression 、k Nearest neighbor algorithm classification and regression 、 From simple linear regression to multiple linear regression 、 From linear regression to logical regression 、 Nonlinear classification and decision tree regression 、 From decision tree to random forest 、 From perceptron to support vector machine 、 From perceptron to artificial neural network 、 Principal component analysis for dimensionality reduction . The examples in this book are all in Python3 Integrated development environment Anaconda3 A typical case that has passed the actual debugging in , At the same time, this book is equipped with the source code and data set of cases for readers' reference .

Important information that readers need to know

This book is a professional book for machine learning , Introduce the basic concepts of machine learning 、 Algorithm flow 、 model building 、 Data training 、 Model evaluation and tuning 、 Necessary tools and implementation methods , The whole process is driven by real cases , Case with Python3 Realization . This book covers data acquisition 、 Algorithm model 、 The whole process of case code implementation and result display , Take the classical algorithm of machine learning as the axis ： Algorithm analysis → Data acquisition → model building → infer → Algorithm evaluation . The cases in this book are representative , It combines theory with practice , And be able to define the goal of machine learning and its effect .

The reader of this book

This book is suitable for big data analysis and mining 、 Beginners of machine learning and artificial intelligence technology 、 Researchers and practitioners , It is also suitable for big data of universities and training institutions 、 Teaching reference for teachers and students of machine learning and artificial intelligence related majors .

Author of this book

Dengliguo , Doctor of computer application, Northeastern University . Guangdong University of technology , Main research direction ： data mining 、 knowledge engineering 、 Big data processing 、 Cloud computing 、 Distributed computing, etc . The author of books 《scikit-learn Machine learning practice 》《Python Data analysis and mining practice 》《Python Big data analysis algorithms and examples 》《Python Machine learning algorithms and applications 》《 Database principle and application （SQL Server 2016 edition ）》.

Contents of this book

1. The first 1 Chapter   An overview of machine learning 1
2. The first 2 Chapter   Data characteristics of machine learning 9
3. The first 3 Chapter   use scikit-learn Estimator classification
4. The first 4 Chapter   naive bayesian classification
5. The first 5 Chapter   Linear regression
6. The first 6 Chapter   use k Nearest neighbor algorithm classification and regression
7. The first 7 Chapter   From simple linear regression to multiple linear regression
8. The first 8 Chapter   From linear regression to logical regression
9. The first 9 Chapter   Nonlinear classification and decision tree regression
10. The first 10 Chapter   Integration method ： From decision tree to random forest
11. The first 11 Chapter   From perceptron to support vector machine
12. The first 12 Chapter   From perceptron to artificial neural network
13. The first 13 Chapter   Principal component analysis for dimensionality reduction

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