Machine Learning : Classification vs Regression Algorithm

As shared in my previous blog, algorithm selection is a very important and confusing part for any Machine Learning project. It is very difficult for a newbie to understand which algorithm suits best for the requirement an data.
Before selecting the algorithm, one needs to understand what type of predictive algorithm is needed.

In most of the ML projects, one would come across situations which would fall under the umbrella of supervised machine learning(we will talk about unsupervised ML in some other article). There are 2 types of supervised algorithms:
1.) Classification : A classification problem is when your output is a category. For example deciding if the person is Rich, Middle Class and poor based on the income and other parameters. In these case output could only be a category.
2.) Regression: A regression problem is when your out is a value. For example predicting the weight of the person based on the appearance parameters.

Once we understand what is the type of our problem we can look for the algorithms related to that side.
Hope this clarifies.

How to learn machine learning?

Machine Learning is the new hype of IT industry and everyone is talking about it. I started learning ML a couple of months back.
I did some hello world models using Python and Microsoft. Spent some time to learn Python(which is fairly easy task), completed a certification and spent descent amount of time.
But even after spending a month, all i knew is some terms with no knowledge. I was not confident that i could handle it.

Although i am still learning but now i understand what machine learning is. It could be done in R, Python, Microsoft or any other. Learning a language or tool is the easiest part of ML, its the concepts that matters.

After doing hands on the language of choice, you would have answer the critical questions like which algorithm to use or which cleansing method to use and lot more.
The pie chart below lists all the things that needs to be done for any ML model.

I will try to cover all the important concepts in this series of articles, which i would feel are important for beginners.

P.S.: You do not have to be good in stats( and you can learn basics during practice), as 99% of us would never need to write a new algorithm. All we need to do is to understand the problem and decide the right set of algorithms for the solution.