Machine Learning
Machine Learning uses algorithms to understand data, learn from it, and then make future prediction or forecasts. Machine Learning is a step ahead of Business Intelligence; it uses a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of historical data and algorithms that give it the ability to learn how to perform the task. Machine Learning came directly from the minds of the evolved from the early days of Artificial Intelligence.
At a high level Machine Learning is of 2 types: Supervised and Unsupervised Learning. Some examples of Machine Learning Algorithm are Linear Regression, Decision Tree, Clustering, Reinforcement Learning, Bayesian Networks and many more.
The Business Challenge
Loan Distribution was a principal offering of the Bank. The major earnings of the Bank come from Loans disbursed and the interest earned. The Bank offered personal and company loans. The Bank wanted to reduce the credit risk and the defaults in loan repayment.
Aziro (formerly MSys Technologies) Predictive Analytics Approach
We proposed predictive analytics solution to measure the probability that a debtor will default. Our objective was to include the score of probability for defaulting as a key component in getting the loans. The score was used as a measure for credit risk of the potential loan customer.
After analyzing the business problem, our focus was on two model types to measure the credit risk:
- Logistic Regression
- Decision Trees
A sample data of Model Scoring and Evaluation to decide which Model to select in Machine Learning 
Deep Learning
“The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”
– Andrew Ng (source: Wired)
Deep Learning has evolved from Artificial Neural Networks which was a form of Machine Learning. Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.
Deep Learning is used in Image Processing. For example, in image processing, an image is broken into a bunch of tiles that are inputted into the first layer of the neural network, which in turn passes the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced.
In earlier days Natural Language Processing(NLP) used statistical methods and Machine Learning. Now NLP is using neural network methods of Deep Learning.
An example of Natural Language Processing(NLP) using Deep Learning is Chat Bots. Chat Bot uses NLP deep learning model to better understand the meaning and context of a message or email or a support ticket sent by customer. We expect to see even more innovative applications of NLP using deep learning in the near future, and expect the machines to bring better customer service as a result.
Following is a CHAT BOT architecture using Natural Language Processing using Machine Learning; and the Diagram Below shows NLP using Deep Learning:
