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Practical Machine Learning Cookbook
Practical Machine Learning Cookbook

Practical Machine Learning Cookbook: Supervised and unsupervised machine learning simplified

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Practical Machine Learning Cookbook

Chapter 1. Introduction to Machine Learning

In this chapter, we will cover an introduction to machine learning and various topics covered under machine learning. In this chapter you will learn about the following topics:

  • What is machine learning?
  • An overview of classification
  • An overview of clustering
  • An overview of model selection and regularization
  • An overview of non-linearity
  • An overview of supervised learning
  • An overview of unsupervised learning
  • An overview of reinforcement learning
  • An overview of structured prediction
  • An overview of neural networks
  • An overview of deep learning

What is machine learning?

Human beings are exposed to data from birth. The eyes, ears, nose, skin, and tongue are continuously gathering various forms of data which the brain translates to sight, sound, smell, touch, and taste. The brain then processes various forms of raw data it receives through sensory organs and translates it to speech, which is used to express opinion about the nature of raw data received.

In today's world, sensors attached to machines are applied to gather data. Data is collected from Internet through various websites and social networking sites. Electronic forms of old manuscripts that have been digitized also add to data sets. Data is also obtained from the Internet through various websites and social networking sites. Data is also gathered from other electronic forms such as old manuscripts that have been digitized. These rich forms of data gathered from multiple sources require processing so that insight can be gained and a more meaningful pattern may be understood.

Machine learning algorithms help to gather data from varied sources, transform rich data sets, and help us to take intelligent action based on the results provided. Machine learning algorithms are designed to be efficient and accurate and to provide general learning to do the following:

  • Dealing with large scale problems
  • Making accurate predictions
  • Handling a variety of different learning problems
  • Learning which can be derived and the conditions under which they can be learned

Some of the areas of applications of machine learning algorithms are as follows:

  • Price prediction based on sales
  • Prediction of molecular response for medicines
  • Detecting motor insurance fraud
  • Analyzing stock market returns
  • Identifying risk ban loans
  • Forecasting wind power plant predictions
  • Tracking and monitoring the utilization and location of healthcare equipment
  • Calculating efficient use of energy
  • Understating trends in the growth of transportation in smart cities
  • Ore reserve estimations for the mining industry

An overview of classification

Linear regression models present response variables that are quantitative in nature. However, certain responses are qualitative in nature. Responses such as attitudes (strongly disagree, disagree, neutral, agree, and strongly agree) are qualitative in nature. Predicting a qualitative response for an observation can be referred to as classifying that observation, since it involves assigning the observation to a category or class. Classifiers are an invaluable tool for many tasks today, such as medical or genomics predictions, spam detection, face recognition, and finance.

An overview of classification

An overview of clustering

Clustering is a division of data into groups of similar objects. Each object (cluster) consists of objects that are similar between themselves and dissimilar to objects of other groups. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Clustering can be used in varied areas of application from data mining (DNA analysis, marketing studies, insurance studies, and so on.), text mining, information retrieval, statistical computational linguists, and corpus-based computational lexicography. Some of the requirements that must be fulfilled by clustering algorithms are as follows:

  • Scalability
  • Dealing with various types of attributes
  • Discovering clusters of arbitrary shapes
  • The ability to deal with noise and outliers
  • Interpretability and usability

The following diagram shows a representation of clustering:

An overview of clustering

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Key benefits

  • • Implement a wide range of algorithms and techniques for tackling complex data
  • • Improve predictions and recommendations to have better levels of accuracy
  • • Optimize performance of your machine-learning systems

Description

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.

Who is this book for?

This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.

What you will learn

  • Get equipped with a deeper understanding of how to apply machine-learning techniques
  • Implement each of the advanced machine-learning techniques
  • Solve real-life problems that are encountered in order to make your applications produce improved results
  • Gain hands-on experience in problem solving for your machine-learning systems
  • Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance
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Language : English
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Table of Contents

14 Chapters
1. Introduction to Machine Learning Chevron down icon Chevron up icon
2. Classification Chevron down icon Chevron up icon
3. Clustering Chevron down icon Chevron up icon
4. Model Selection and Regularization Chevron down icon Chevron up icon
5. Nonlinearity Chevron down icon Chevron up icon
6. Supervised Learning Chevron down icon Chevron up icon
7. Unsupervised Learning Chevron down icon Chevron up icon
8. Reinforcement Learning Chevron down icon Chevron up icon
9. Structured Prediction Chevron down icon Chevron up icon
10. Neural Networks Chevron down icon Chevron up icon
11. Deep Learning Chevron down icon Chevron up icon
12. Case Study - Exploring World Bank Data Chevron down icon Chevron up icon
13. Case Study - Pricing Reinsurance Contracts Chevron down icon Chevron up icon
14. Case Study - Forecast of Electricity Consumption Chevron down icon Chevron up icon

Customer reviews

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Ram Jun 12, 2017
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So far explanations are not easy to understand & steps are skipped.
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