Getting Started with Machine Learning

Explore the Machine Learning Tutorial Series and learn ML from Scratch

Practical Learning with Machine Learning Projects

Strengthen your portfolio and get your dream Data Science job by implementing these real-world Machine Learning Projects. Display your true potential to recruiters and become the next data scientist.

machine learning R Project on Uber Data Analysis

R Project on Movie Recommendation using machine learning

machine learning Project on Customer Segmentation using R

R data science Project on credit card fraud detection using ML

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Exploring the Idea of Machine Learning

Let’s take a look at some facts about Machine Learning and its philosophies.

In 1959, computer gaming and AI pioneer Arthur Samuel coined the term at IBM. This is a field of computer science that makes use of statistical techniques to give computer systems the ability to learn without being explicitly programmed. This comes through a quest for artificial intelligence- as they say, necessity is the mother of invention. Many researchers like to claim this is the best way to progress toward human-level AI. With machine learning, we build algorithms with the ability to receive input data and use statistical analysis to predict output while updating output as newer data become available.

We often make use of techniques like supervised, semi-supervised, unsupervised, and reinforcement learning to give machines the ability to learn.

What is Machine Learning?

Machine Learning is the scientific study of algorithms that involves usage of statistical models that computers utilize to carry out specific tasks without any explicit instruction. It relies on patterns and other forms of inferences derived from the data.

Machine Learning algorithms are built on top of a mathematical model that makes use of a sample data known as “training data” for making decisions without any explicit programming.

creator of machine learning - Arthur Lee Samuel

Arthur Lee Samuel