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Your Comprehensive Guide for Markov Models, Reinforced Learning, Model Evaluation, SVM, Naives Bayes Classifier

Machine Learning: For Beginners, Book 3

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Your Comprehensive Guide for Markov Models, Reinforced Learning, Model Evaluation, SVM, Naives Bayes Classifier

De: Ken Richards
Narrado por: Jacob Ford
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Acerca de este título

"Artificial Intelligence, deep learning, machine learning - whatever you're doing if you don't understand it - learn it. Because otherwise you're going to be a dinosaur within 3 years." (Mark Cuban)

"Machine learning is about taking the data that anyone might have - whether it's a sports franchise or an industrial manufacturer - and using algorithms to actually reason over the data and to predict outcomes that a businessperson can use to make better decisions.” (Christopher Matthews)

Discover and learn all about the machine learning in this audiobook. You will appreciate and learn more about advanced machine learning algorithms that are not presented in earlier books.

Do you know that you are highly likely part of testing datasets for companies in their machine learning model training, application, and mobile apps? Don’t you want to learn more about the framework that institutions and companies are using for machine learning?

The reality is very real. Data collections are everywhere in everything that we do and these behaviors that we exhibit and share willingly with application owners will be used to improve our user experience and improve our daily life such as the usage of Siri, Alexa, Cortana, Google Assistant, and many other applications.

“I think it’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better. I think that’s the single best piece of advice: Constantly think about how you could be doing things better and questioning yourself.” (Elon Musk)

Aren’t our data collected the feedback loop for companies to improve their products and offerings?

In this book, you will learn more about advanced machine learning algorithms that are not presented in earlier books.

©2018 Ken Richards (P)2018 Ken Richards
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