Data Science Algorithms In A Week

Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set

Build strong foundation of machine learning algorithms In 7 days.About This Book* Get to know seven algorithms for your data science needs in this concise, insightful guide* Ensure you're confident in the basics by learning when and where to use various data science algorithms* Learn to use machine learning algorithms in a period of just 7 daysWho This Book Is ForThis book is for aspiring data science professionals who are familiar with Python and have a statistics background. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skill set.What You Will Learn* Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems* Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series* See how to cluster data using the k-Means algorithm* Get to know how to implement the algorithms efficiently in the Python and R languagesIn DetailMachine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.Style and approachMachine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.

Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a week Know when and where to apply data science algorithms using this guide Book Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learn Understand how to identify a data science problem correctly Implement well-known machine learning algorithms efficiently using Python Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy Devise an appropriate prediction solution using regression Work with time series data to identify relevant data events and trends Cluster your data using the k-means algorithm Who this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set

Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda Key Features -Use Anaconda to find solutions for clustering, classification, and linear regression -Analyze your data efficiently with the most powerful data science stack -Use the Anaconda cloud to store, share, and discover projects and libraries Book Description Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R. What you will learn Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda Use the package manager conda and discover, install, and use functionally efficient and scalable packages Get comfortable with heterogeneous data exploration using multiple languages within a project Perform distributed computing and use Anaconda Accelerate to optimize computational powers Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud Tackle advanced data prediction problems Who this book is for Hands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It’s also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

This book is Part II of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part II contains Chapters 4 through 6 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

一周掌握常用的数据科学算法 本书提供完整源代码实现,案例式辅助讲解 Key Features 7种数据科学算法高效进行数据分类及预测 从简单的例子开始讲解算法的基本概念与知识 通过对案例进行扩展以讲解一些特殊的分析算法 Book Description机器学习的应用是高度自度化且自动修正的。学习到的数据越多,机器学习应用需要的人工干预就越少。为了解决现实世界中复杂的数据问题,科学家们开发出专门的机器学习算法来解决这些问题。数据科学正是通过算法和统计分析来帮助读者从现有数据中获取新知识的。 本书将解决如何精确且高效地进行数据分类及预测的问题。本书主要讲解7种数据科学算法,有k最近邻算法、朴素贝叶斯算法、决策树、随机森林,k-means聚类、回归分析和时间序列分析。 此外,你还会掌握如何对数据进行预聚类,以便针对大型数据集进行优化和分类。最后,你将了解如何根据数据集中的现有趋势来预测数据。本书的各章还有配套的练习题,以帮助你夯实内容,扩展相关知识。 本书适合数据分析人员、机器学习领域的从业人员以及对算法感兴趣的读者阅读。读完本书后,你将了解如何选择机器学习算法进行聚类、分类或回归,并知道选择哪种算法来解决实际问题。What you will learn 如何使用朴素贝叶斯、决策树和随机森林进行分类并准确地解决复杂问题; 正确识别数据科学问题并使用回归分析和时间序列分析设计合适的预测解决方案; 如何使用k-means算法对数据进行聚类; 如何使用Python和R语言有效地实现算法。 Who this book is for 本书适合数据分析人员、机器学习领域的从业人员以及对算法感兴趣的读者阅读。