What is a good first-implementation for learning machine learning?

Question

I find learning new topics comes best with easy implementation to code to get the idea. This is how I learned genetic algorithms and genetic programming. What would be some good introductory programs to write to get started with machine learning?

Preferably, let any referenced resources be accessible online so the community can benefit.

Answer 1

Choosing the programming language:

To taste the real flavor of machine learning, we need to get our hands dirty with coding part too. It won’t be like, will learn just the concepts and leave the implementation work to others.

The moment we start learning machine learning we frequently listen to the debate Python, or R is the best programming language for machine learning. Both the languages well at a top level and have loopholes too. If you already know any of the programming languages you can continue with that.

If you are completely new to the programming world you can start with Python Programming language.

Answer 2

Just pick a couple of interesting data sets, begin by implementing some standard classifiers and examining their performance. This is a good shortlist of classifiers to learn:

• k-nearest neighbors
• linear discriminant analysis
• decision trees 
• support vector machines
• boosting (with stumps)
• naive bayes classifier

With the Iris data set and one of the languages I mention, you can easily do a mini-study using any of the classifiers quickly.