Machine Learning is the most in-demand technology in today’s market. Its applications range from self-driving cars to predicting deadly diseases such as ALS. The high demand for Machine Learning skills is the motivation behind this blog. In this blog on Introduction To Machine Learning, you will understand all the basic concepts of Machine Learning and a Practical Implementation of Machine Learning by using the R language.

The term Machine Learning was first coined by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms of technological advancements.

If you browse through the net about ‘what is Machine Learning’, you’ll get at least 100 different definitions. However, the very first formal definition was given by Tom M. Mitchell:

In simple terms, 

Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed to do so. In the sense, it is the practice of getting Machines to solve problems by gaining the ability to think.

This Introduction To Machine Learning covered following topics:

• Need For Machine Learning
• What Is Machine Learning?
• Machine Learning Definitions
• Machine Learning Process
• Types Of Machine Learning
• Type Of Problems Solved Using Machine Learning
• Practical Implementation Of Machine Learning

Need For Machine Learning

Ever since the technical revolution, we’ve been generating an immeasurable amount of data. As per research, we generate around 2.5 quintillion bytes of data every single day! It is estimated that by 2020, 1.7MB of data will be created every second for every person on earth.

With the availability of so much data, it is finally possible to build predictive models that can study and analyze complex data to find useful insights and deliver more accurate results.