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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 reinforcement learning

Reinforcement learning is the problem of getting an agent to act in the world so as to maximize its rewards. It is about what to do and how to map situations to actions so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. The two most important distinguishing features of reinforcement learning are trial and error and search and delayed reward. Some examples of reinforcement learning are as follows:

A chess player making a move, the choice is informed both by planning anticipating possible replies and counter replies.
An adaptive controller adjusts parameters of a petroleum refinery's operation in real time. The controller optimizes the yield/cost/quality trade-off on the basis of specified marginal costs without sticking strictly to the set points originally suggested by engineers.
A gazelle calf struggles to its feet minutes after being born. Half an hour later it is running at 20 miles per hour.
Teaching a dog a new trick--one cannot tell it what to do, but one can reward/punish it if it does the right/wrong thing. It has to figure out what it did that made it get the reward/punishment, which is known as the credit assignment problem.

Reinforcement learning is like trial and error learning. The agent should discover a good policy from its experiences of the environment without losing too much reward along the way. Exploration is about finding more information about the environment while Exploitation exploits known information to maximize reward. For example:

Restaurant selection: Exploitation; go to your favorite restaurant. Exploration; try a new restaurant.
Oil drilling: Exploitation; drill at the best-known location. Exploration; drill at a new location.

Major components of reinforcement learning are as follows:

Policy: This is the agent's behavior function. It determines the mapping from perceived states of the environment to actions to be taken when in those states. It corresponds to what in psychology would be called a set of stimulus-response rules or associations.
Value Function: This is a prediction of future reward. The value of a state is the total amount of reward an agent can expect to accumulate over the future, starting from that state. Whereas rewards determine the immediate, intrinsic desirability of environmental states, values indicate the long-term desirability of states after taking into account the states that are likely to follow, and the rewards available in those states.
Model: The model predicts what the environment will do next. It predicts the next state and the immediate reward in the next state.

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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