Learn basics of Machine Learning
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. LiveEdu.tv is a place where you can find machine learning projects and learn new skills to improve job prospects. Watch premium live projects with the help of the pro subscription.
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.
Machine Learning Tools
Like any subfield of artificial intelligence, machine learning requires tools to work efficiently and effectively. These tools will help you gain an advantage while working and make work easy. Let’s list the best machine learning tools out there. We will focus only on open source tools because they are easy to acquire.
- Scikit-learn Scikit-learn python library is extremely popular in Machine learning community. It offers features on tons of packages including SciPy, NumPy, matplotlib, etc.
- Mahout Mahout works great with Hadoop. It has a good collection of algorithms that can be used separately without the interference of Hadoop.
- MLlib MLlib is a simple-to-use Apache’s machine learning library for Hadoop and Spark. It offers a good collection of algorithms and useful data types. The library scales nicely and is designed to run at top speed.
- GoLearn GoLearn is a machine learning library for the GO programming language. It is simple to use and extremely customizable.
- Weka Weka is a simple product that collects all the Java machine learning algorithms for easy availability. It is free to use and works great with data mining.
Education Ecosystem Machine Learning Project Creators
If you are wondering how to get started on Machine Learning, then I will recommend you to follow Machine learning Project Creators on Education Ecosystem. Let’s list the top 5 machine learning Project Creators on Education Ecosystem.
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 Education Ecosystem. 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.
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.
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.
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.
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
This book describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
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.
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.
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 Education Ecosystem. If you are interested, check the Education Ecosystem machine learning Project Creators 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 Community
The community of Machine Learning is big. There are many websites you can find a community and become part of it. Let’s list some of them below.
- Stack Exchange
Stack Exchange is a QA answer community where you can ask and share information easily. Cross Validated is the ML part of Stack Exchange
Here you can find information about Data Science, ML related stuff in a Hackernews format.
- Education Ecosystem
Here you can find all the awesome Project Creators who love to share their knowledge about Machine learning.
Machine learning Gurus
Jeffrey Hawkins is a well-known entity in Machine learning. He is the founder of the Palm Computing and Handspring. He works surround neuroscience and use of machine learning. He is also well known for his prediction framework theory of the brain.
Sebastian Thrun is a scientist, robotics expert, educator from the land of Germany. His major work includes working on the self-driving car and creating Udacity, the home of online learning. Earlier, he worked as a Google VP and has also been a Computer science professor at Stanford University.
Andrew Yan-Tak NG is a Chinese computer scientist and is currently working as a chairman and cofounder of Coursera. He has also been an associate professor at Stanford University. He has worked on Baidu, the Chinese search engine.
Zoubin Ghahramani is a British-Iranian researcher and is currently working as a professor at the University of Cambridge. He is extremely active in academics and has been awarded a double major degree in Computer Science and Cognitive Science in 1990.
Geoffrey Everest Hinton is a computer scientist and cognitive psychologist. He divides his work between the University of Toronto and Google.
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.