Do you know about Jeopardy! quiz show where a computer named Watson was able to beat world champions? No! Go watch it! Yes? Nice! Isn’t it a feat as grand as the one achieved by Deep blue (chess computer); if not less?
I am always interested in how such advanced computers was built. In case of Watson, It’s fascinating how technologies such as Natural language processing, machine learning & artificial intelligence backed by massive compute & storage power was able to beat two human world champions. And as a person interested in analytic’s and Big Data – I would classify this technology under Big Data and Advanced Data Analytics where computer analyzes lots of data to answer a question asked in a natural language. It also uses advanced machine learning algorithms. To that end, If you’re interested in getting an overview of what went into building WATSON, watch this:
If you’re as amazed as I am, considering sharing what amazed you about this technology via comment section:
For the Past couple of months, One of the things that I have thought about is “What is the Difference Between Machine Learning & Data Mining”. I have Studied Data Mining and Advanced Data Mining concepts at both Undergraduate and Graduate level and recently I started learning about Machine Learning via Coursera.org – I was curious to know the difference between the two similar/inter-related fields. After, spending time understanding what Machine Learning is – Here’s what I am thinking:
When I learned Data Mining – The focus was on Taking a Data-set and using (more than one) Algorithm(s) to detect Patterns in the data-set. I am studying machine learning – Here, we’re asked to write algorithms (and build models). So To me, Data Mining seems to be deal with practical aspects of putting Machine Learning algorithms to use.
When I took Data Mining courses – I didn’t write algorithms. But learned what different Data Mining Algorithms can do and what kind of patterns each algorithm helps us find. In machine learning class, my focus is to learn how to write the algorithms (build the model) and optimize it so that it can predict well.
Also, in machine learning the goal is clear – the questions are mostly like “Build a model from Past Data that predicts X “. whereas I remember, For our Graduate Level class, My professor gave our Team a data-set of “fatal accident data” and said “Go play with it!”
These were my experiences. What are your experiences with Data Mining, Machine Learning – and how do you differentiate between these two fields which are similar in more than one ways?