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Packt Fundamentals Of Machine Learning With Scikit Learn Tutoriale
Категория: Уроки, Видео Уроки | Опубликовал(а): michaa47 | 14 02 2019

Packt Fundamentals Of Machine Learning With Scikit Learn Tutoriale
FUNDAMENTALS OF MACHINE LEARNING WITH SCIKIT LEARN TUTORIAL-OXBRiDGE


Genre:Training
Size: 374.49 MB




Description
University of OXford & University of camBRiDGE

is a collective term for characteristics that the two institutions share

Fundamentals of Machine Learning with scikit-learn

Published by: Packt
Mar 2018

Lecture Size: 08x50
Length: +2 hrs
Lecture Date: 10/2018
Lecture Link: https://www.safaribooksonline.com/
https://www.packtpub.com: | Build strong foundation for entering the world of Machine Learning
| and data science with the help of this comprehensive guide
|
| About This Video
|
| Get started in the field of Machine Learning with the help of
| this solid, concept-rich, yet highly practical guide
| Your one-stop solution for everything that matters in
| mastering the whats and whys of Machine Learning algorithms and
| their implementation
| Get a solid foundation for your entry into Machine Learning by
| strengthening your roots (algorithms) with this comprehensive
| guide
|
| In Detail
|
| As the amount of data continues to grow at an almost
| incomprehensible rate, being able to understand and process data
| is becoming a key differentiator for competitive organizations
| Machine Learning applications are everywhere, from self-driving
| cars, spam detection, document searches, and trading strategies
| to speech recognition. This makes machine learning well-suited to
| the present-day era of big data and data science. The main
| challenge is how to transform data into actionable knowledge
|
| In this course you will learn all the important Machine Learning
| algorithms that are commonly used in the field of data science
| These algorithms can be used for supervised as well as
| unsupervised learning, reinforcement learning, and semi-supervised
| learning. A few famous algorithms that are covered in this book
| are: Linear regression, Logistic Regression, SVM, Naive Bayes, K
| Means, Random Forest, and Feature engineering. In this course, you
| will also learn how these algorithms work and their practical
| implementation to resolve your problems
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