As per the definition by Wikipedia, Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. To my understanding, Machine Learning can be best explained as the answer to the question – how can we build computer systems that automatically improve with experience.

Machine Learning intends to the learning of the techniques using which we can program in a better way without using a hardcoded set of rules. It is the technique to make the computer learn by observation and learning instead of following the particular set of hardcoded instructions.

Today’s reality is all about data. It is estimated that since the dawn of time, humans had created approximately 130 EXABYTES of data.
1 EXABYTE = 1024 PETABYTES
1 PETABYTE = 1024 TERABYTES
1 TERABYTE = 1024 GIGABYTES
Huge numbers! It is estimated that in 2020, the total data that humans will have generated till dawn will be equal to an estimate of 40,900 exabytes.
It is not practically possible for humans to process this huge data. Humans can directly understand only a specific portion of this data. The rest of data is continuously being created but not used. For example, with the help of mobiles and GPS, you are generating the GPS data which cannot be directly used or processed by humans. However, when this data is properly analyzed by efficient sets of machine learning tools and technique, this data is processed, analyzed and used to track your movement which in turn can be used for many applications as we know today.

This is exactly where machine learning comes into place. Machine learning can help to understand the data which was unused till now and it can help to improve living and understanding of the world. If you notice deeply, machine learning is already being used in many places as of today. Facebook uses it to detect faces automatically in the pictures you post. Amazon and other e-commerce websites use it to target customer interest specific products.

The concept of Machine Learning is very broad. You can use machine learning to predict something or you can have interactive machine learning where you the teach the machine to interact with you – like the face recognition feature we talked above.

We all have heard of Artificial Intelligence. In my viewpoint, artificial intelligence is just a fancy word for machine learning and machine learning is a technical term for artificial intelligence. Also, there is a lot of similarity between data science and machine learning. A data scientist is also a machine learning scientist.

Machine Learning can be classified into various types – Pattern Recognition, Deep Learning, Clustering, etc. The basic areas of machine learning include :

1. Regression – To predict something.
2. Classification – To classify something.
3. Clustering

Apart from this, there are some more sophisticated areas of machine learning like :

1. Associated Rule Learning
2. Re-enforcement Learning
3. Natural Language Processing(texts, sentiments, etc)
4. Deep Learning(facial recognition, etc)
5. Dimensionality Reduction

This concludes the brief explanation of the terminology of Machine Learning. Machine learning is an important aspect with the viewpoint of knowledge, research as well as career. This much of content was enough for me to dive towards the field of machine learning. I will explain the basic setup required to start coding for machine learning with languages like Python and R in the next post.