Machine Learning

Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.

Writing software is the bottleneck, we don’t have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming at Higher level.

=>Normal Programming: Data and program is run on the computer to produce the output.

=>Machine Learning: Data and output is run on the computer to create a program. This program can be used in normal programming.

A breakthrough in machine learning would be worth ten Microsofts .

— Bill Gates, Former Chairman, Microsoft

Machine learning is like farming or gardening. Seeds is the algorithms, nutrients is the data, the gardner is you and plants is the programs.

Machine learning is a way of teaching computers to recognize patterns, and it’s particularly useful in making sense of large amounts of data. The key idea is to let a computer learn by example instead of programming it with specific rules.

Key Elements of Machine Learning

There are tens of thousands of machine learning algorithms and hundreds of new algorithms are developed every year.

Every machine learning algorithm has three components:

=>Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.
=>Evaluation: the way to evaluate candidate programs (hypotheses). Examples include accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence and others.
=>Optimization: the way candidate programs are generated known as the search process. For example combinatorial optimization, convex optimization, constrained optimization.

All machine learning algorithms are combinations of these three components. A framework for understanding all algorithms.

These are the basic information that are covered in the introduction to most machine learning courses and in the opening chapters of any good textbook on the topic.

Although targeted at academics, as a practitioner, it is useful to have a some little bit of idea in these concepts in order to better understand how machine learning algorithms behave in the general sense.