A hands on guide to machine learning to get you upto speed on the basic concepts and ideas.
Welcome and congratulations on your decision to learn ML. But What is Machine Learning? In recent times machine learning is one of the buzz world and is often used interchangeably with Artificial intelligence and Data Science, so lets establish the base line.
Lets start by defining what each term means.
AI is a very broad term and is the super set of ML. In simple terms AI is a computer program that tries to mimic, develop and demonstrate human behavior. It can be as simple as a software playing chess or an autonomous car.
Machine learning is one of the hottest topic right now. It refers to a computer ability to learn from sets of data and build up solution to a given problem without human intervention. ML is broadly classified into tow types
- Supervised Learning: One which uses labelled data to map input to output. Example include Decision trees, Linear Regression etc. (Don't worry we will cover these later)
- Unsupervised Learning: One which doesn't require labelled or classified raining data but instead uses unlabeled data to perform a desired task. Popular example of Unsupervised learning are the Clustering algorithms.
Deep learning is a type of Supervised Machine learning algorithm and is probably the most popular form of Machine Learning right now
Data science is an inter-disciplinary field that requires skills and concepts used in disciplines such as statistics, machine learning, visualization, etc. One could say a data scientist is just a fancy term for the business analyst. So finally we can summarize the above using the following Venn diagram.
Mayur Selukar @mrselukar
Machine Learning Engineer Student at Udacity.
- Udacity Machine Learning Engineer Nanodegree
- This post from acadguild.