If you looking for data mining techniques, third edition then you are right place. We are searching for the best data mining techniques, third edition on the market and analyze these products to provide you the best choice.
Best data mining techniques, third edition
1. Data Mining Techniques, Third Edition: For Marketing, Sales, and Customer Relationship Management
Description
The leading introductory book on data mining, fully updated and revised!
When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition - more than 50 percent new and revised - is a significant update from the previous one and reveals how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics, such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.
- Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems.
- Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately.
- Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more.
- Provides best practices for performing data mining using simple tools such as Excel.
Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
2. Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
Description
Solve real-world data problems with R and machine learning
Key Features
- Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond
- Harness the power of R to build flexible, effective, and transparent machine learning models
- Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz
Book Description
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
What you will learn
- Discover the origins of machine learning and how exactly a computer learns by example
- Prepare your data for machine learning work with the R programming language
- Classify important outcomes using nearest neighbor and Bayesian methods
- Predict future events using decision trees, rules, and support vector machines
- Forecast numeric data and estimate financial values using regression methods
- Model complex processes with artificial neural networks the basis of deep learning
- Avoid bias in machine learning models
- Evaluate your models and improve their performance
- Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow
Who this book is for
Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
Table of Contents
- Introducing Machine Learning
- Managing and Understanding Data
- Lazy Learning Classification Using Nearest Neighbors
- Probabilistic Learning Classification Using Naive Bayes
- Divide and Conquer Classification Using Decision Trees and Rules
- Forecasting Numeric Data Regression Methods
- Black Box Methods Neural Networks and Support Vector Machines
- Finding Patterns Market Basket Analysis Using Association Rules
- Finding Groups of Data Clustering with k-means
- Evaluating Model Performance
- Improving Model Performance
- Specialized Machine Learning Topics
3. Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) by Jiawei Han (2011-07-06)
4. Data Mining: Practical Machine Learning Tools and Techniques - International Edition
Description
***** INTERNATIONAL EDITION ***** ***** INTERNATIONAL EDITION ***** ***** INTERNATIONAL EDITION *****5. Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition
Description
Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.
What is new in the Third Edition:
- The current chapters have been completely rewritten.
- The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.
- Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).
- Includes SAS subroutines which can be easily converted to other languages.
As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
6. Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5, 3rd Edition
Description
Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications
Key Features
- Build independent machine learning (ML) systems leveraging the best features of R 3.5
- Understand and apply different machine learning techniques using real-world examples
- Use methods such as multi-class classification, regression, and clustering
Book Description
Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models.
This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models can be diagnosed and understood.
By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work.
What you will learn
- Prepare data for machine learning methods with ease
- Understand how to write production-ready code and package it for use
- Produce simple and effective data visualizations for improved insights
- Master advanced methods, such as Boosted Trees and deep neural networks
- Use natural language processing to extract insights in relation to text
- Implement tree-based classifiers, including Random Forest and Boosted Tree
Who this book is for
This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.
Table of Contents
- Preparing and Understanding Data
- Linear Regression
- Logistic Regression
- Advanced Feature Selection in Linear Models
- K-Nearest Neighbors and Support Vector Machines
- Tree-Based Classification
- Neural Networks and Deep Learning
- Creating Ensembles and Multiclass Methods
- Cluster Analysis
- Principal Component Analysis
- Association Analysis
- Time Series and Causality
- Text Mining
- Appendix A- Creating a Package
7. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3th (third) edition
Description
Text Book8. Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition
Description
Gain useful insights from your data using popular data science tools
Key Features
- A one-stop guide to Python libraries such as pandas and NumPy
- Comprehensive coverage of data science operations such as data cleaning and data manipulation
- Choose scalable learning algorithms for your data science tasks
Book Description
Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.
The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.
By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
What you will learn
- Set up your data science toolbox on Windows, Mac, and Linux
- Use the core machine learning methods offered by the scikit-learn library
- Manipulate, fix, and explore data to solve data science problems
- Learn advanced explorative and manipulative techniques to solve data operations
- Optimize your machine learning models for optimized performance
- Explore and cluster graphs, taking advantage of interconnections and links in your data
Who this book is for
If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
Table of Contents
- First Steps
- Data Munging
- The Data Pipeline
- Machine Learning
- Visualization, Insights, and Results
- Social Network Analysis
- Deep Learning Beyond the Basics
- Spark for Big Data
- Appendix A: Strengthen Your Python Foundations
