Machine Learning

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

De: William Bahl

Narrado por: William Bahl

Duración: 2 h y 5 m

Resumen del Editor

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.

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.

An excellent book!

An excellent book! The book is quite fit for beginners in machine learning. I am an experienced professional but a novice in ML. I knew next to nothing about machine learning and artificial intelligence. I found this very informative and I will definitely listen to it a few more times

I totally recommend

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. This course will help me understand machine hence improve my work on a daily basis.

THE zero to hero Machine Learning book.

Bahl is a great instructor. He brings his passion to the course and his helpful examples and explanations are invaluable to students of the course. I would highly recommend this course on Machine Learning for Data Science.

This Book is the Real Deal

Cloud computing is the cure to this ailment; you no longer have to risk running into such situations due to its ability to automatically scale the storage amount you need. This is where the idea of load balancing comes in. There’s no need to pay for a large amount of storage that will sit unused throughout the year except during particular events. With load balancing, the cloud storage system will distribute storage space as needed, and the process is done nearly instantly. When the system detects that you are closing in on the storage threshold, it will automatically increase the amount of storage you need. Once the need for storage decreases, the system scales it down. Because of this factor, storage is becoming more affordable. One of the biggest issues with big data is that it requires equally big storage.

A Must-have for the Machine Learning Practitioner

The data scientist needs to feed the training data, including the inputs, outputs, and labels. Once the learning phase is complete and we have our predictions, the algorithm can be reused on another similar dataset. Basically, supervised machine learning systems learn the same way as the student who goes through various exercises and comes up with his own ideas and conclusions that can be reapplied to other exercises.