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

Machine Learning: For Beginners, Book 3

By: Ken Richards

Narrated by: Jacob Ford

Length: 1 hr and 48 mins

Publisher's summary

"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

ml knowledge

It a good overview book, however it would be good if difficult topics would be described without using domain specific terms, which assumes you already have ML knowlage. If the terms are used, they should be defined upfront. If variuos specific methods are mentioned, it would be good to provide a comparison and overview of those. I general it is good book, which I would recommmend reading as a starting point for its structured approach.

5 star for this

Just had a good grasp of what ML and ML algorithms are. How they’re subdivided into three more categories and how useful are they. Moreover, an in-depth discussion on Markov Models and Naives Bayes classifier are included. I’m giving this a 5-star rating as it’s a mix of core concepts and problem-solving.

Helpful Information!

Good guide on machine learning. This series of books has really got me up and running on all the concepts needed. Pleased I went ahead and gave this one a listen. I find this stuff to be fascinating. Well worth checking out.

distinct explanation

Appreciate the author’s distinct explanation considering this subject is very technical. There’s a short intro to Markov Models and Naïve Bayes classifier technique which I’ve been meaning to learn more about.

informative guide

Another informative guide by Ken Richards. It’s more advance than the previous one I got and offered topics that are quite complicated but I got them right away as the author uses simple terms, explanations, and modern approaches.

i'm favoring this ML guide

I’m bound to get the part 1 and 2 of this ML series. One of the parts I like on this book is the discussion on Marov Models. The author was very specific on its intro and on the examples given. Aside from this, I’m having fun learning about the different ML models like SVM. I’m not a pro at it yet, but I believe I’ll be soon with the help of this audiobook.

an in-depth knowledge

Tis an in-depth knowledge in machine learning. This has to be one of my favorite books and I’m thinking of grabbing the first two books and access them.

cannot go wrong with this book.

Any additional comments?

The authors gently guide you through Machine Learning and Data Science. They start from the essentials, including how to install all the required software and make the first steps, touching all the important math, and finish showing how it can be applied to real life problems.

meaty info

The audiobook has some meaty info that I just can’t ignore. Yes, I’m curious about the framework that companies are using for machine learning, thus I’m reading this cover to cover.