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scikit-learn机器学习(第2版) by Posts & Telecom Press,Gavin Hackeling Pdf
掌握Python机器学习的有效工具,搞定scikit-learn的必备指南 Key Features 本书内容涵盖多种机器学习模型 讨论了数据预处理、超参数优化和集成方法等主题。 Book Description近年来,Python语言成为了广受欢迎的编程语言,而它在机器学习领域也有着卓越的表现。scikit-learn是一个用Python语言编写的机器学习算法库,它可以实现一系列常用的机器学习算法,是一个不可多得的好工具。 本书通过14章内容,详细地介绍了一系列机器学习模型和scikit-learn的使用技巧。本书从机器学习的基础理论讲起,涵盖了简单线性回归、K-近邻算法、特征提取、多元线性回归、逻辑回归、朴素贝叶斯、非线性分类、决策树回归、随机森林、感知机、支持向量机、人工神经网络、K-均值算法、主成分分析等重要话题。 本书适合机器学习领域的工程师学习,也适合想要了解scikit-learn的数据科学家阅读。通过阅读本书,读者将有效提升自己在机器学习模型的构建和评估方面的能力,并能够高效地解决机器学习难题。What you will learn 学会构建用于文档分类、图像识别、广告检测等任务的系统 学到如何使用scikit-learn类库的API从类别变量、文本和图像中提取特征,如何评估模型的性能 如何提升模型的性能建立直觉 掌握在实践中运用scikit-learn构建高效模型所需的技能,并能够通过实用的策略完成高级任务。 Who this book is for 程序开发人员、一般的Python用户、机器学习、人工智能领域的读者、普通高校计算机相关专业的学生
The The Machine Learning Workshop by Hyatt Saleh Pdf
Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook Description Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you’ll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms. What you will learnUnderstand how to select an algorithm that best fits your dataset and desired outcomeExplore popular real-world algorithms such as K-means, Mean-Shift, and DBSCANDiscover different approaches to solve machine learning classification problemsDevelop neural network structures using the scikit-learn packageUse the NN algorithm to create models for predicting future outcomesPerform error analysis to improve your model's performanceWho this book is for The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron Pdf
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Pdf
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Learning Scikit-Learn by Raul Garreta,Guillermo Moncecchi Pdf
The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.
Python Scikit-Learn for Beginners by Ai Publishing Pdf
Python for Data Scientists -- Scikit-Learn SpecializationScikit-Learn, also known as Sklearn, is a free, open-source machine learning (ML) library used for the Python language. In February 2010, this library was first made public. And in less than three years, it became one of the most popular machine learning libraries on Github.Scikit-learn is the best place to start for access to easy-to-use, top-notch implementations of popular algorithms. This library speeds up the development of ML models.The main features of the Scikit-learn library are regression, classification, and clustering algorithms (random forests, K-means, gradient boosting, DBSCAN, AND support vector machines). The Scikit-learn library also integrates well with other Python libraries, such as NumPy, Pandas, IPython, SciPy, Sympy, and Matplotlib, to fulfill different tasks.Python for Data Scientists: Scikit-Learn Specialization presents you with a hands-on, simple approach to learn Scikit-learn fast.How Is This Book Different?Most Python books assume you know how to code using Pandas, NumPy, and Matplotlib. But this book does not. The author spends a lot of time teaching you how actually write the simplest codes in Python to achieve machine learning models.In-depth coverage of the Scikit-learn library starts from the third chapter itself. Jumping straight to Scikit-learn makes it easy for you to follow along. The other advantage is Jupyter Notebook is used to write and explain the code right through this book.You can access the datasets used in this book easily by downloading them at runtime. You can also access them through the Datasets folder in the SharePoint and GitHub repositories.You also get to work on three hands-on mini-projects: Spam Email Detection with Scikit-Learn IMDB Movies Sentimental Analysis Image Classification with Scikit-Learn The scripts, graphs, and images in the book are clear and provide easy-to-understand visuals to the text description. If you're new to data science, you will find this book a great option for self-study. Overall, you can count on this learning by doing book to help you accomplish your data science career goals faster.The topics covered include: Introduction to Scikit-Learn and Other Machine Learning Libraries Environment Setup and Python Crash Course Data Preprocessing with Scikit-Learn Feature Selection with Python Scikit-Learn Library Solving Regression Problems in Machine Learning Using Sklearn Library Solving Classification Problems in Machine Learning Using Sklearn Library Clustering Data with Scikit-Learn Library Dimensionality Reduction with PCA and LDA Using Sklearn Selecting Best Models with Scikit-Learn Natural Language Processing with Scikit-Learn Image Classification with Scikit-Learn Hit the BUY NOW button and start your Data Science Learning journey.
Machine Learning with scikit-learn Quick Start Guide by Kevin Jolly Pdf
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook Description 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 teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is for 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.
Ultimate Machine Learning with Scikit-Learn by Parag Saxena Pdf
TAGLINE Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn KEY FEATURES ● Comprehensive coverage of complete predictive modeling lifecycle, from data munging to deployment ● Gain insights into the theoretical foundations underlying powerful machine learning algorithms ● Master Python's versatile Scikit-Learn library for robust data analysis DESCRIPTION “Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn. Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes. Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines(SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis. By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence. WHAT WILL YOU LEARN ● Master fundamental data preprocessing techniques tailored for both structured and unstructured data ● Develop predictive models utilizing a spectrum of methods including regression, classification, and clustering ● Tackle intricate data challenges by employing Support Vector Machines (SVMs), decision trees, and ensemble learning approaches ● Implement advanced anomaly detection methodologies and explore emerging techniques like neural networks ● Build efficient data pipelines optimized for handling big data and streaming analytics ● Solidify core machine learning principles through practical examples and illustrations WHO IS THIS BOOK FOR? This book is tailored for experienced and aspiring data scientists, machine learning engineers, and AI practitioners aiming to enhance their skills and create impactful solutions using Python and Scikit-Learn. Prior experience with Python and machine learning fundamentals is recommended. TABLE OF CONTENTS 1. Data Preprocessing with Linear Regression 2. Structured Data and Logistic Regression 3. Time-Series Data and Decision Trees 4. Unstructured Data Handling and Naive Bayes 5. Real-time Data Streams and K-Nearest Neighbors 6. Sparse Distributed Data and Support Vector Machines 7. Anomaly Detection and Isolation Forests 8. Stock Market Data and Ensemble Methods 9. Data Engineering and ML Pipelines for Advanced Analytics Index