Machine Learning cover art

Machine Learning

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

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Machine Learning

By: William Bahl
Narrated by: William Bahl
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About this listen

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 Computer Science Machine Theory & Artificial Intelligence Machine Learning Data Science Artificial Intelligence Thought-Provoking

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Most relevant  
Probably the best publicly available self contained resource on the subject.

Analyzing the data will tell you what kind of algorithm to use to interpret it, but before you can actually use the algorithm, you’ll likely need to do some prep work on your data. Properly preparing your data helps to ensure that you get the results you’re looking for and that the algorithm functions the way you intend.

The number and types of features and attributes you want to consider will also have an impact on how much preparation work you need to do on your data. If there are a lot of missing features or outliers, cleaning up the data can help your models run more efficiently. You may also need to transform the data by compiling it or scaling so it’s easier for the program to process.

You may also find that you don’t want to use every piece of data that you have available to train your algorithm. Curating the dataset that the algorithm learns from can help to direct the types of situations it predicts well. You may choose to leave out entire portions of the data, or simply to have the program ignore certain features.

Probably the best publicly available self ........

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I wanted to improve my life but it's so hard. At this time one of my friends told me about this voice book and I bought it. If you want to learn about machine learning then get this book. its will guide you about that. learning approach.

A educative book

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This course is amazing and even though I do not have everything down yet, it gave me a good insight into the basics.

Best Book on the topic!

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Great course! I have enjoyed the material and Bahl's teaching skills. I recommend this course to anyone who is wanting a comprehensive perspective of machine learning.

Great book with ML and TF combination

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Highly interesting and informative, feels good to have completed this course since it gives me insights and meets my deliverable requirements.

Brilliant and Precise

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When collecting a dataset, it is common also to have experts who can suggest the fields and attributes that are important. If that is not possible, then the easiest way is the brute force technique. This involves taking account of all available information, with the assumption that all features are already isolated. This type of data collection is not suitable for the process of induction since it has lots of noise and features that are missing. This makes it need a more detailed pre-processing step that is cumbersome.

Great clarity, nice depth

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Correctly using machine learning algorithms can allow you to make stunningly accurate predictions about trends and patterns you’re likely to encounter in the future. This can help your business to avoid potential hazards before they become a problem for your bottom line or to identify potential opportunities before your competition.

Very practical, to my knowledge, the perfect level

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When working with statistics and probability during machine learning, we need to consider independence. One of the variables that you can work with here is to figure out how much independence is inside the problem. When you work with these random variables, you are going to find out that they are going to be independent of what the other random variables are, as long as the distribution that you have doesn’t change if you take a new variable and try to add it into that equation. To make this one work a bit better, you are going to need to work with a few assumptions in concerns to the data you are using with machine learning. This makes it a bit easier when you already know about independence. An excellent example to help us understand what this is all about is a training sample that uses j and I, and are independent of any underlying space when the label of sample I is unaffected by the features sample j. No matter what one of the variables turns out, the other one is not going to see any change or be affected, if they are independent.

Connecting statistics knowledge to Machine Learn..

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In machine learning, there will be times when you need to make assumptions and use the experience that you have, either in that area or a similar area, to get things done. In some cases, you may even need to do some experimenting to figure out what you want to do. But machine learning can help to get this done.

One of the easiest to understand books on a comple

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Listener received this title free

This book is extraordinary cause all of this books tips are really helpful. If you know little to nothing about AI, this is the book for you. I think this guide will help everyone with Machine learning step-by-step Guide from Beginners. I am satisfied to listen to artificial intelligence. AI is using all over the projects and machine. Inspired by the previous example of productive learning.

This book is extraordinary

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