Machine Learning

A Beginners Guide to History, Development and Future Possibilities of Machine Learning

De: William Bahl

Narrado por: William Bahl

This book is designed to be an introduction to machine learning algorithms for a complete beginner. It starts with an explanation of exactly what machine learning algorithms are and then walks you through the languages and frameworks used to create them.

Studying machine learning is considered to be quite challenging due to the impression that special talent is required or some unachievable level of mathematics is needed in order to understand the various algorithms and techniques. The purpose of this book is to show you that anyone can learn to become a machine learner and put the theory into practice.

This book provides you with all the information you need to understand machine learning at a beginner level. You will get an idea on the different subjects that are linked to machine learning and some facts about machine learning that make it an interesting subject to learn. Without further ado let’s get started.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2019 William Bahl (P)2019 William Bahl

This is a very informative audio book

This is a very informative audio book. I am completely listening this audio book it's helpful for developing my skill. This audio book Machine Learning that guide all beginners to history, development and future possibilities of machine learning is very helpful for understanding. I am happy to get this audiobook. Thanks to the great author!

Good price at only $6.95

Businesses need to make sure that they are using the right kind of terminology to ensure that they are getting the most out of this process. You can call these things deep learning, computer vision, or machine learning; but do not call it AI. All of these do sometimes find themselves under the umbrella of artificial intelligence as a term, but they are different.

This is an excellent book for beginners

This is an excellent introduction to machine learning for anyone but will likely be more satisfying for those who may be familiar with computer programming and basic statics. I would like to tell everybody, that this book was perfect for those who wants to jump in the machine learning field. Lots of real-world scenarios, perfect example is one of the best features of these audible.

Very good understanding and breadth

This audio book methods the fundamentals with a light language to listen to. I am keen on machine learning and my associate prescribed me to pursue this audio book. As a beginner to the topic, I got everything i needed to know in enough depth and breadth. Very good understanding and breadth.

Must Have Audio

In supervised learning, you are going to train the machine to do what you want, and things have to be well labeled in order to see them work. You want to be able to train a machine in a manner that helps you to predict how long it is going to take to make the predictions that you want. Out of the different machine learning algorithms that are out there, you will find that supervised learning is going to be easier to work with than unsupervised learning and two of the techniques that work with this are classification and regression. The biggest challenge that programmers are going to notice with this is that if they get some input features that are irrelevant to this, then you are going to get inaccurate results. It can help you to collect up the data that you want, or even to produce a new output of data from your previous experiences. Supervised learning, according to training decision trees and networks, is the most common technique used. Machine learning technology is the only way you will be able to get these programs to work. Regular coding is not able to do this complex task. The system needs to read through your request, look through millions of websites, and then pick out the one that matches up to your needs. And in the beginning, it may not do the best job picking out what you want. But thanks to the machine learning technology behind it, these programs can learn your search preferences and will learn exactly what you are looking for based on your choice.

A very in-depth book on the topic

Machine Learning is brilliantly explained in this outstanding book. Though there are many similar books out there, this is one of the best books for studying Machine Learning. The methods of this book were so good and useful for beginners. I will share this book to all my friends. A very in-depth book on the topic. I am learning math, the techniques, and the intuition behind it as well.

Look no Further!

One of the reasons Weka is so popular is its versatility. Its broad compatibility with different operating systems is a part of this, but it also has a wide variety of different uses. It is efficient when put to use for more explorative, lighter uses, but also is robust enough to handle more extensive analysis and design processes. Classification problems are the interface’s main strength, but it has a fairly extensive library of very modern algorithms, including decision trees, rule mining, ensemble, and cluster methods.

Easy to understand for a self-motivated learners

A must for those who want to learn the practical application of machine learning in predictive data analytics! I recommend this book to anyone who is trying to understand the theoretical details of classical machine learning. I strongly urge the authors to do so in the next edition. A lot in the area is learned by doing, by using good software development practices. It is very easy to understand for a self-motivated learner.

Machine learning, code, examples

A lot of people without experience in machine learning make the mistake of thinking that an unsupervised algorithm doesn’t require training, but this is not the case. An unsupervised algorithm still has to be given training data before you can start using it for modeling. The difference is that the training data given to unsupervised algorithms is not labeled. The goal of an unsupervised algorithm is not necessary to make direct predictions about the likelihood or causes of specific factors, but rather a more exploratory process. They are best used to find the structure within a large quantity of unlabeled data or to discover connections between factors that were previously unknown. Unsupervised algorithms are trained using unlabeled data. The program is given the inputs and told to analyze them, but doesn’t know the correct outputs that correspond to them. Rather than looking for the right answer, it learns to look for potential solutions within the data itself. There are several methods that one can choose from to be able to handle any kind of data missing. There have been researches that have identified the advantages and disadvantages of this technique. Let's look at the instant selection. It is used to cope with learning infeasibility from large data sets. An optimization problem that is instance selection tries to mine quality as it minimizes the size of another sample. Data is reduced, and data mines are maintained through an algorithm to work efficiently when exposed to big data sets.

Best Machine Learning book I own

Data engineers prepare the entire infrastructure for machine learning. The infrastructure can range from designing, developing and managing the entire entire data pipeline. Data engineers can also help to set up reporting tools for a refined analysis process. Most of the analysis that can be done on top of the architecture include the Hadoop, Pig, MapReduce, Hive, and Redshift. Data engineers can also be viewed as regular software engineers with a background knowledge in machine learning.