Machine learning algorithms : fundamental algorithms for supervised and unsupervised learning / Joshua Chapmann

By:

Chapmann, Joshua [author]

Material type: Text

TextPublication details: [Place of publication not identifed] : Independently published, c2017Description: 78 pages ; 23 cmISBN:

9781548307752

Subject(s):

MACHINE LEARNING

LOC classification:

Q 325.5 .C43 2017

Summary: Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms. In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people. The book shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms (K-Nearest Neighbour, Na ve Bayes, Regressions) and Unsupervised Learning Algorithms (Support Vector Machines and Decision Trees).

Item type:

Books

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Holdings
Item type	Current library	Home library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	National University - Manila	LRC - Main General Circulation	Machine Learning	GC Q 325.5 .C43 2017 (Browse shelf(Opens below))	c.1	Available		NULIB000015992

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Previous	GC Q 325.5 .A98 2019 c.1 Automated machine learning : methods, systems, challenges /	GC Q 325.5 .B47 2021 Introduction to machine learning /	GC Q 325.5 .B53 2019 c.1 Machine learning A-Z : introduction to AI digital brains of the future /	GC Q 325.5 .C43 2017 Machine learning algorithms : fundamental algorithms for supervised and unsupervised learning /	GC Q 325.5 .E36 2022 Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow /	GC Q 325.5 .G47 2017 Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems /	GC Q 325.5 .H69 2020 Deep learning for coders with fastai and pytorch : AI applications without a PhD /	Next

Machine learning algorithms :, Machine

Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms. In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people. The book shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms (K-Nearest Neighbour, Na ve Bayes, Regressions) and Unsupervised Learning Algorithms (Support Vector Machines and Decision Trees).

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