Your Comprehensive Guide for Markov Models, Reinforced Learning, Model Evaluation, SVM, Naives Bayes Classifier
- Machine Learning: For Beginners, Book 3
- Narrated by: Jacob Ford
- Length: 1 hr and 48 mins
- Unabridged Audiobook
- Release date: 02-13-18
- Language: English
- Publisher: Ken Richards
Regular price: $6.95
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"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.
Customer ReviewsMost Helpful
By LB on 03-03-18
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.