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    Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases (English Edition)
    Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases (English Edition)
    Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases (English Edition)
    Ebook230 pages56 minutes

    Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases (English Edition)

    By Deepti Chopra

    ()

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    About this ebook

    This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms.

    You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail.

    At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.
    LanguageEnglish
    PublisherBPB Online LLP
    Release dateMay 7, 2021
    ISBN9789389423624
    Building Machine Learning Systems Using Python: Practice to Train Predictive Models and Analyze Machine Learning Results with Real Use-Cases (English Edition)
    Author

    Deepti Chopra

    Deepti Chopra 是 Banasthali 大学的助理教授。她的主要研究领域是机器翻译。她在各种期刊和会议上发表了多篇论文，并担任多个会议和期刊的项目委员会委员。

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      Book preview

      Building Machine Learning Systems Using Python - Deepti Chopra

      CHAPTER 1

      Introduction

      Machine learning is one of the applications of artificial intelligence. Machine learning may be defined as the ability of the system to learn automatically through experience without being explicitly programmed. It is based on the development of programs that can access data and use this data to perform learning on their own. In this chapter, we will discuss the classification of machine learning, the various challenges faced in machine learning, and the applications of machine learning.

      Structure

      History of machine learning

      Classification of machine learning

      Challenges faced in adopting machine learning

      Applications

      Objectives

      Understanding the origin of machine learning

      Understanding the classification of machine learning algorithm

      Challenges faced in machine learning

      Applications of machine learning

      History of machine learning

      In 1940s, the first manually-operated computer, ENIAC (Electronic Numerical Integrator and Computer), was invented. At this time, the word computer was used which meant, 'a machine having intensive numerical computation capabilities'. Since 1940s, the idea was to build a machine that could mimic human behavior of learning and thinking. In 1950s, the first computer game program was developed that could beat the checkers world champion. This helped checker players in improving their skills. At this time, Frank Rosenblatt invented Perceptron, which is a very simple classifier. Machine learning became popular in 1990s when probabilistic approaches of AI were born as a result of the combination of statistics and computer science. Because of the large data available, scientists started building intelligent systems that could analyze and learn from a large amount of data. For example, the IBMs Deep Blue could beat the World Chess Champion, Garry Kasparov. Machine learning is a kind of algorithm in which the software applications can accurately predict the outcomes without being explicitly programmed. The basic essence of machine learning is to build algorithms that, on receiving input data, predicts the output using statistical analysis and updates the output as the new data is made available. The term Machine learning was coined by an American scientist, Arthur Samuel, in 1959 who had expertise in computer gaming and artificial intelligence. According to Arthur Samuel, "It gives computers the ability to learn without being explicitly programmed". According to Tom Mitchell in 1997, "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience

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