Machine learning is widely used in today’s data driven world. From Alexa, Siri, Social Media, and Search Engines to Traffic Predictions, Online Transportation Network, Video Surveillance, Malware Filtering, Online Customer Support etc. – Machine Learning is literally everywhere in the vicinity. It has made programming simpler and software accurate. The demands for machine learning professionals are rising due to this.

What is Machine Learning?

Machines Learning is the power of machines to perform tasks without being explicitly programmed. It uses stored data to learn and later use this learning to make human work easy.

According to Tom M. Mitchell, an American Computer Scientist, “A computer program is said to learn from experience E with respect to some class of task T and performance measure P if its performance at task in T, as measured by P, improves with experience E.” In other words, machines are learning by themselves. Don’t freak out! Machine learning depends on programming, algorithms and logic to collect data and find pattern among these data. Afterward, machine learning uses the data to make prediction. The bigger the volume of data, more accurate is the prediction. One example of data learning in which we all can relate to – If you stay engaged to a certain type of story in Facebook, then you will definitely cross related posts when you next time log in. This is because Facebook algorithms use your data history to predict the stories, videos or memes you like.

Limited not only to data prediction to enhance user experience, machine learning is a field bigger than that. Machine Learning has its applications in the healthcare industry too. Certain disease like cancer is hard to predict in the initial stages. With the help of artificial intelligence and machine learning, it has become reasonably easy to predict initial stage cancer. IBM Watson is helping in this scenario. It is using integrating cognitive computing with genome based tumor sequencing to make early diagnosis.

There are other applications of machine learning in arena like Neural Networks, Self Organizing Maps, Fraud Detection in Finance, Product Recommendation in Retail, Dynamic Pricing in Travelling and many more.

How to start a course in Machine Learning?

There are many online channels providing courses to learn MS in Machine Learning. Some prerequisites are there before you start a course in machine learning. You need programming language knowledge either in C++, JAVA, R or Python. Mostly, industrial works are done on Python and R. If you have knowledge over C++ or JAVA, then you will learn Python in no time. Unlike C++ and JAVA, Python and R syntax are easy.

You also need to have good grip on probability and statistics, machine learning algorithms, data modeling and evolution, software engineering and system design.

At the time of learning, you will understand how easy it actually is – machine learning. Mainly, software engineering skills are the key to become a great Machine Learning Engineer.

Career Opportunities

Machine Learning can gift you jobs that you were dreaming of. Here are some fields where a Machine Learning Engineer works –

1. Software Engineering

2. Software Development

3. Machine Learning Engineer

4. Data Architecture

5. Research Scientist (AI)