Python Machine Learning: A Practical Beginner's Guide to Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras

By Brandon Railey

★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★

 

If you responded yes to any of the above questions, you have come to the right place.

Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that.

Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it?

★★Apart from this, you will also learn more about★★

 

♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter

♦ The Numerous Applications Of Machine Learning And Deep Learning

♦ The Best Practices For Picking Up Neural Networks

♦ What Are The Best Languages And Libraries To Work With

♦ The Various Problems That You Can Solve With Machine Learning Algorithms

♦ And much more...

Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network?

So, what are you waiting for? Grab a copy of this book now!

• Language: English
• Publisher: Brandon Railey
• Release date: Apr 25, 2019
• ISBN: 9781386372585

Book preview

Introduction

Machine learning has transformed the way a business and organization operates. It is important for every business to understand patterns and trends in complex data to succeed or maximize profits. This understanding is becoming a key attribute that a company must use to grow in a challenging environment. You can use Python, and the many underlying libraries and directories to derive some key insights about the data you have. The unique capabilities of the language make it easier for you to build some sophisticated algorithms and models that will allow you to stem some insights about the data set.

From a business standpoint, machine learning can be used to understand how a customer thinks and behaves. Businesses can also use machine learning to predict the future profits or losses that the company may face. They can also use it to see when they should stock a specific product or when they should release a new product. Let us look at how a retailer can improve his or her sales through machine learning. The retailer can see when his or her customers purchase a specific product, and increase the stock for that product during that season. Walmart had realized that its customers wanted to purchase strawberries during a specific season, and the retailer increased the stock of strawberries in the store. This helped them improve on their profits. The same can be said about e-commerce websites as well.

Machine learning, cognitive computing and artificial intelligence are some terms that most people read about when it comes to advanced analytics. These subjects give a company an edge or an advantage over other businesses in the same industry. Every business leader is now facing an unanticipated and new competitor, and these leaders are looking for ways to develop new strategies that will help them prepare their business for the future. Businesses can always try a variety of strategies, but they all will boil down to one fact – you must always follow the data. This book will help you learn more about how you can use machine learning to delve into the data, and how you can derive some predictions from that data. You will also learn what you should think about when it comes to machine learning, and also see what how you can analyze data using different machine learning algorithms.

Machine learning is a topic that is discussed in almost every organization in any industry. Most organizations choose to use machine learning algorithms to identify new ways to make use of the data that they collect. So, why should you add machine learning to your processes? If you use the right machine learning algorithms, you can predict how a business or how a customer will act. You can always predict what will happen next. When you constantly add new data to the algorithm or model, the machine will learn from that data and update the solution as often as it can. The value of doing this is evident – if you use the correct data in a machine learning model and constantly update that data, you can predict the future with ease.

Machine learning is a subset of artificial intelligence, and this method of learning allows any machine to gather information from input data. This means that we will not be using any explicit commands or code to explain to the system about what it will need to do. That being said, it is not easy to implement machine learning models in a business. A machine learning will use different algorithms that learn only from the data, and derive methods that can be used to improve, describe or predict the outcomes. When the machine takes in more data, you can ensure that you train the machine well to produce accurate results.

Once you train the model, and give it some new data, it should provide you with an output. For instance, if you use a predictive algorithm, you will create a predictive model. When you feed that model with any new data, the machine will give you an output that is based on the training data set that you had initially used. Machine learning is now being used extensively to develop some analytics model.

You may have interacted with many machine learning applications in the past. For instance, if you visit Amazon or Flipkart and begin to view products, you will be presented with other products that are similar to what you were looking at. These recommendations are not coded into the system, but are suggestions that are given to you through a machine learning model. The model will look at all your history and use that information to present you with similar products.

There are different types of machine learning algorithms that a business can use depending on the type of information or data it has available. For example, if the business has a labeled data set, it can use any supervised machine learning algorithm to predict the future. On the other hand, if the business did not have a labeled data set, it could use some unsupervised machine learning algorithm to train the model to derive an output. This book covers some of the most commonly used machine learning algorithms.

Throughout this book, you will gather information on what machine learning is, and how you can build your very own model in Python using SciKit learn. If you want to learn more about machine learning, deep learning and neural networks, and how you can build these models in Python, this book is invaluable. This book covers the use of the NumPy and Pandas libraries in Python and will help you build a machine learning model and a neural network in SciKit-learn using those libraries. You will come across some examples of code that will help you understand the steps you will need to follow when it comes to building a machine learning application or a neural network. It is only when you understand these codes fully that you should start with building your applications.

I hope this book will help you gather more insights on what machine learning is, and how you can use it to analyze different data sets.

Chapter One: An Introduction to Machine Learning

Machine learning is a process through which machines with inbuilt artificial intelligence or AI often learn processes, probability, identifying problems and solutions. Such machines are used for data analysis, recognition, predictions, diagnosis and projections. They learn from training data, a sample database that is identical to the entire population, to identify different patterns within the data and to use those patterns, as well as help find solutions to problems. Out of the various methods of learning mechanisms used to help a machine learn, unsupervised machine learning, reinforcement learning and supervised machine learning are the most common amongst them. The following chapters will cover these concepts.

In a layman's language, the change in the structure of a machine to enhance its performance is a classic example of how a machine learns.

Let’s say if a machine must predict which team will win the Barclays Premier League. The programmer will train or feed the machine with historical data about the team’s performance and the player’s performances. Using such information and data the machine can identify a pattern and correlations between the data points or variable in the data set. The machine can then use these patterns and predict which team will win the Barclays Premier League.

Skeptics often wonder why machines must learn since they are pretty aware of what these machines are capable of. Facebook has used machine learning and AI to develop a machine trained with specific vocabulary dataset. It’s scary how this machine used the alphabets that are found in the data to create its own language, a language humans find difficult to understand. If multiple machines learn this language, then they can communicate amongst themselves. Regardless of the outcome, Google and Facebook are training machines to learn through continuous feeding of datasets.

There are many reasons as to why machine learning is important. Psychologist are using some of these concepts of machine learning to help understand and better evaluate human beings and their mind.

Subjects Involved in Machine Learning

Machine learning uses concepts from various subjects. The field of machine learning is developing as you read this book and hence the subjects mentioned are not an exhaustive list. However, the subjects mentioned below along with the concepts are the foundation of machine learning.

Statistics

Regression, clustering, data analysis and hypothesis testing are few of the concepts in statistics that are the foundation of machine learning. Most machine learning algorithms use these concepts to train machine which is a common problem in both statistics and machine learning. Training is the process where various sample datasets help machine pull out information about the population. The Machines store this information and use it to predict or project futuristic outcomes and values. The machine can also identify a problem and its solution to that particular problem using the training dataset. Another problem that is common to both statistics and machine learning is the identification of the values of a function at a given point. Solutions to such problems are the instances that the machine uses to evaluate the estimation of future events that often use data from the past.

Brain Modeling

Neural networks in the brain are often referred to as a concept that is