Machine learning - an exciting field of study

Machine learning is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. ML is the field of computer science which makes the machine capable of learning on its own without being explicitly programmed. When exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the code every single time.
There is an abundance of data right now, and data that is being collected and stored is growing. This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. ML covers significant ground in various verticals – including image recognition, medicine, cyber security, facial recognition, and more. As an increasing amount of businesses are realising that business intelligence is profoundly impacted by machine learning, and thus are choosing to invest in it. There is so much information coming at us from email, social networking, blogs, RSS and podcasts. It’s hard to impossible to keep up. Machine Learning methods provide the tools to locate and recommend the most relevant content to you in order to overcome information overload.

Machine learning is everywhere. You might be using it and not be aware about it. Some examples:
Virtual Personal Assistant
Siri, Alexa, Google are virtual personal assistants. These assist in finding information when asked over voice. ML is an integral part of the functioning of personal assistants as they collect and refine the information on the basis of your previous queries. Later this refined dataset is used to give results that are tailored to your preferences

Facial recognition
Your phone unlocks when you look at it because the camera in your phone recognizes unique features and projections on your face using image processing (part of ML) to identify that the person unlocking the phone is not someone else but you.

Email spam filter The email spam filter uses a
supervised machine learning model to filter out spammy emails from your mailbox.

Basic knowledge of programming languages such as Python or R
Good knowledge of statistics and probability
Understanding of linear algebra and calculus and multivariate calculus
Data modeling to find variations and patterns in a given dataset
Graph Theory
Optimization Methodology (Lagrange multipliers)

Introduction to Machine Learning
Deep Reinforcement Learning
Probabilistic Graphical Models
Advanced Machine Learning
Data Analysis
Convex Optimization
Probability & Mathematical Statistics
Algorithms for NLP, Neural Networks for NLP
Multimodal Machine Learning
Multimedia Databases and Data Mining
Algorithms in the Real World
Computer Vision
Regression Analysis

Almost every sector of various industries- healthcare, education, retail, manufacturing, supply chain and logistics, BFSI, and even governance, is leveraging the applications of AI and ML in some way or the other. Demand for skilled professionals in AI and ML increasing. A report by TMR notes that MLaaS (Machine learning as a Service) is predicted to grow from to US$19.9 billion by the end of 2025, from US$1.07 billion in 2016. Career Opportunities in ML are:
ML Engineer
Data Scientist
NLP Scientist
Business Intelligence Developer
Human-Centered Machine Learning Designer

Machine Learning is broadly divided into three main areas
Supervised ML
Means training the machine learning model just like a coach trains a batsman. In Supervised Learning, the machine learns under the guidance of labeled data i.e. known data. This known data is fed to the machine learning model and is used to train it. Once the model is trained with a known set of data, you can go ahead and feed unknown data to the model to get a new response.

Unsupervised machine learning
Means the ml model is self-sufficient in learning on its own - there is no such provision of labeled data. Unknown data is fed to the machine learning model and is used to train the model. The model tries to find patterns and relationships in the dataset by creating clusters in it.

Reinforcement ML
The machine learns from a hit and trial method. Whenever the model predicts or produces a result, it is penalized if the prediction is wrong or rewarded if the prediction is correct. Based on these actions the model trains itself.