Machine Learning Introduction
Machine learning is a field in Computer Science which allows computers to learn without explicitly programming them to do so. It is a subcategory of Artificial intelligence which deals with the study of computational learning theory and pattern recognition. A lot of algorithms are used to make machine learning a reality. Machine learning involves the utilization of data for both prediction and decision making.
The application of machine learning can be seen from companies who uses it to automate tasks such as email filtering, computer vision, etc. Thus, automated systems using machine learning become perfect with time and make less error.
History of Machine Learning
Machine learning came about as a result of advancement in the field of Artificial Intelligence. AI was an academic field which paved the way for new ideas. Researchers then came up with the idea of machine learning from data and adapting accordingly. The idea was successful with the help of neutral networks and probabilistic reasoning.
Emphasis on knowledge-based logical approach leads to the division of machine learning and Artificial Intelligence. Further research made it clear that Machine learning needed a proper categorization. During the 1990s, this categorization was done, and Machine Learning became a subcategory of Artificial Intelligence with a goal to give machines the ability to learn. Machine learning now makes more use of probability theory and statistics than symbolic approach.
LiveEdu Machine Learning Streamers
If you are wondering how to get started on Machine Learning, then I will recommend you to follow Machine learning streamers on LiveEdu. Let’s list the top 5 machine learning streamers on LiveEdu.
Streamers!
Machine Learning Best Books
There are many Machine Learning books online. The best way to start with Machine learning is to read books and stream your machine learning projects on LiveEdu.tv. Let’s go through some of the best books on “machine learning”. These books are categorized into Beginner, Intermediate, and Advanced. You can pick from any of the books that best suits you.
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by Oliver Theobald
This book is a beginner's introductory book which provides a plain English introduction Artificial Intelligence, Big Data, Downloading Free Data Sets, Regression, Support Vector Machine Algorithms, Deep Learning/Neural Networks, Data Reduction, Clustering, Association Analysis and Machine Learning Careers.
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by Andreas C. MüllerSarah Guido
This is a book to learn practical ways to build your own machine learning solutions. You will also learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.
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by Stephen Marsland
This book contains theories which are backed up by practical example. It also contains text demonstration of how to use algorithms that make up machine learning methods but also provides the background needed to understand how and why these algorithms work.
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by Stuart Russell, Peter Norvig
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
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by Kevin P. Murphy
This book offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.
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by Peter Flach
This book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorization and ROC analysis. Particular attention is paid to the central role played by features.
Machine Learning Projects
The best way to learn is to evolve yourself with Projects. Let’s look at some of the best machine learning projects that you can follow. You can also find machine learning projects on LiveEdu. If you are interested, check the LiveEdu machine learning streamers section for more information.
Scikit-learn is a popular Python library that is made purely for machine learning. You can dive deep into the repository and explore it. You can also use it to do some interesting projects.
Explore this project!
Machine Learning cars Conferences
As Machine Learning is a trending topic in the market, there are many conferences out there that you can attend. Let’s list some of the best machine learning conferences out there.