[PDF] Machine Learning with R Free Download

[message] Brief Description [PDF] Machine Learning with R Free Download by Brett Lantz | Publisher : Packt Publishing | Category : Computers...

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    • Brief Description
      • [PDF] Machine Learning with R Free Download by Brett Lantz | Publisher : Packt Publishing | Category : Computers & Internet | Tags : Algorithms, Analysis, Big Data, Programming, Social Network | ISBN-10 : 1782162143 | ISBN-13 : 9781782162148
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    • Book Image
      • Machine Learning with R by Brett Lantz, Publisher : Packt Publishing
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    • Complete Book Description
      • Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications


        • Harness the power of R for statistical computing and data science
        • Use R to apply common machine learning algorithms with real-world applications
        • Prepare, examine, and visualize data for analysis
        • Understand how to choose between machine learning models
        • Packed with clear instructions to explore, forecast, and classify data

        In Detail

        Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of “big data” and “data science”. Given the growing prominence of R a cross-platform, zero-cost statistical programming environment there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

        “Machine Learning with R” is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.

        How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

        We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

        “Machine Learning with R” will provide you with the analytical tools you need to quickly gain insight from complex data.

        What you will learn from this book

        • Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
        • Use R to prepare data for machine learning
        • Explore and visualize data with R
        • Classify data using nearest neighbor methods
        • Learn about Bayesian methods for classifying data
        • Predict values using decision trees, rules, and support vector machines
        • Forecast numeric values using linear regression
        • Model data using neural networks
        • Find patterns in data using association rules for market basket analysis
        • Group data into clusters for segmentation
        • Evaluate and improve the performance of machine learning models
        • Learn specialized machine learning techniques for text mining, social network data, and “big” data


        Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.

        Table of Contents

        Chapter 1: Introducing Machine Learning
        Chapter 2: Managing and Understanding Data
        Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
        Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
        Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
        Chapter 6: Forecasting Numeric Data – Regression Methods
        Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
        Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
        Chapter 9: Finding Groups of Data – Clustering with k-means
        Chapter 10: Evaluating Model Performance
        Chapter 11: Improving Model Performance
        Chapter 12: Specialized Machine Learning Topics

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    • Book Details
      • Book Name : Machine Learning with R

        Edition : 1

        Author : Brett Lantz

        Publisher : Packt Publishing

        Category : Computers & Internet

        ISBN-10 : 1782162143

        ISBN-13 : 9781782162148

        ASIN : 1782162143

        Pages : 396

        Language : English

        Publish Date : October 25, 2013
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