How to learn machine learning from scratch?

How to learn machine learning from scratch?Machine learning is the buzz word these days and everybody wants to know something about it. In times to come machine learning will be a concept that everyone who needs to stay competitive will have to know about.What is Machine Learning?

Traditional programs take data as input and produces data as output.

However a machine learning algorithm takes data as input but produces a program as an output. This machine generated program can now take new data, process it and produce output data.

Machine learning algorithms automate the process of creating programs using historical data. In simple words, it gives computers the capability to extract knowledge from data and store it for future judgement.

Wikipedia defines machine learning as…

Machine learning is the subfield of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed".

This brings us to our first introspective question…

Which kind of machine learner are you?

Depending on the role you have in your organization or the role you aspire for, you might fall in one of the three different category of machine learners.

1. Businesses user

Business user is involved in the day to day running of a business. They run the operations and are responsible for defining and executing the business processes of a company. In traditional companies, the executives, operations team and managers fall in this category.

For this kind of user a high level understanding (non technical) of what Machine Learning can do and what it can’t do is beneficial. They need just enough information to equip themselves to determine whether they will see return on investment on machine learning or not?

Here are some examples of successful use cases of machine learning:

• Traditionally for customer support operation teams spend lot of dollars on costly human resources. Machine learning can automate menial operational tasks like customer support.
• Machine learning can analyze tons of usage data (big data) and make remarkable suggestions on business tactics that can be applied to increase revenue.
• To know more interesting use cases read our blog 8 great applications of Machine Learning

2. Machine Learning Engineers & Data Scientists

This learner is someone who will apply machine learning to real life problems. They are the ones who will be the consumers of all machine learning frameworks like Watson, Spark or Sci-kit learn.

They will have a flare for playing with data. They will love to gather data, clean it, augment it with missing information and then use it for machine learning. They are also called machine learning engineers or data scientists.

Industry is hungry for machine learning engineers and data scientists who can apply machine learning algorithms to help the business reduce costs or expand their revenue streams.

This group will not be responsible for creating algorithms. They will users of existing machine learning libraries to solve the problem at hand.

They will be required to understand the strengths and the weaknesses of different machine learning algorithms. They will have to know how a given algorithm behaves in different situations and what is the algorithm best suited for a given type of problem.

They will be responsible for using programming (mostly Python, R, Scala, Java, etc. ) for gathering data from across the organization and public sources, cleaning it and then massaging it to be fed into the machine learning algorithms.

These users will combine art and science to solve the given business problem. Lot of their time will go in trial and error approaches before they arrive at an optimum solution.

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