Journey to Machine Learning { Lesson 1 => Data Science }

Holla ladies and gentlemen. Been a long time yeah? So, I never knew I was a big fan of Artificial Intelligence until I started to see the series – Person of Interest. And not until recent times, I saw myself falling in love with talking machines – ROBOTS. As we all know, Artificial Intelligence is actually wide and I was confused for a while on what to focus on. Even though my final year project was a Dental Expert System, I knew I wanted to focus on something else apart from Expert systems. I made some research on UNCLE GOOGLE and after a period of one month, I had two options: Artificial Neural Networks or Machine Learning. After rigorous research on both fields and chat with experts in the field, I finally settled for Machine Learning. Yayyy, No more confusion. Let drums roll

According to Wikipedia, Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. It’s also interesting to know that Google has an open source machine learning platform known as TensorFlow. Also, on the 23rd of March, Google made its , which is used by Google Photos, Translate, and Inbox, available to developers today.

At our time and age, it is really hard to find a problem where machine learning is not already applied — machine learning is practically everywhere, in business applications and science. You might want to check this Quora post where we have so many examples of how Machine learning can be applied. So after contacting experts in the field, I was advised to start with Data science, after all, Machine Learning needs Data to study patterns, recognize them and predict the right information about the data. Fast forward to learning Data Science, I did a tutorial on python(via codeacademy) and moved to bigdatauniversity to follow their learning paths.

The courses are divided into Lessons and a Lesson has various videos on different topics. For today’s edition of Tech Thursday, I’ll be starting from:

According to the instructor; Murtaza Haider who is the author of the book: Getting Started with Data Science. The summary of a Data Scientist is someone who works with data and the tools to develop insight to make better-informed decisions whileBig Data is a dataset or data structure.

Who can be a Data Scientist? Anyone with expertise in at least one programming language, statistical theory and exposure, and an overall solid fundamental in Mathematics can be a Data Scientist.

Examples of Data Science in Action

• Students rating Professors data
• Getting facts about big houses selling more and more interestingly that the addition of a bedroom to a house is better than adding a bathroom.
• Study on the type of people that cheats.
• To detect the best time to purchase an airline ticket at a cheap price thereby increasing your wealth rate.

Which programming language should you choose as a Data scientist?

This has been a tough one for me  because prior to taking this course, I’ve been contemplating on which language to learn between python or R. But now that I know what can be achieved with each, I’m confident to settle for one with the intention that I know what I’m doing. Well, according toMurtaza Haider; If you dealing with statistics and structured data, choose R because it’s perfect for structured data while Python is excellent for non-structured data and text mining. Scala is used for very wide big data and sparks. An Example of non-structured data are tweets from Twitter.

And that brings us to the end of the Lesson 1 of Data Science. Yes, I know we’re not talking code yet. It’s very important to know that the theoretical aspect of Data Science is vital to know before we begin the practical aspect of it. Until next time. Hasta la vista!!!

As published from: TechInPink