Kind of problems which can be solved using machine learning

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Machine Learning problems can be divided into 3 broad classes: 

  • Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future – you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:
    • Classification Problems: When you want to classify outcomes into different classes. For example – whether the floor needs cleaning/mopping is a classification problem. The outcome can fall into one of the classes – Yes or No. Similarly, whether a customer would default on their loan or not is a classification problem that is of high interest to any Bank
    • Regression Problem: When you are interested in answering how much – these problems would fall under the Regression umbrella. For example – how much cleaning needs to be done is a Regression problem. Or what is the expected amount of default from a customer is a Regression problem





  • Unsupervised Machine Learning: There are times when you don’t want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example – a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here



  • Reinforcement Learning: Reinforcement Learning is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future. 





What are the Different algorithms used in Machine Learning?

  • Supervised Learning
    • Linear Regression
    • Logistic Regression
    • k-nearest neighbors
    • Decision Trees
    • Random Forest
    • Gradient Boosting Machines
    • XGBoost
    • Support Vector Machines (SVM)
    • Neural Networks
  • Unsupervised Learning
    • k means clustering
    • Hierarchical clustering
    • Neural Network
  • Reinforcement Learning


What kind of data is required to train a machine learning model?


Everything which you see, hear and do is data. All you need is to capture that in the right manner. In fact, 90% of the data in this Universe has been created in the last 18 months.

Data can broadly be classified into two types:

  1. Structured Data: Structured data typically refers to data stored in a tabular format in databases in organizations. This includes data about customers, interactions with them, and several other attributes, which flow through the IT infrastructure of Enterprises.
  2. Unstructured Data: Unstructured Data includes all the data which gets captured, but is not stored in the form of tables in enterprises. For example – letters of communication from customers or tweets and pictures from customers. It also includes images and voice records.

Machine Learning models can work on both Structured as well as Unstructured Data. However, you need to convert unstructured data to structured data first.

Endnotes

In this article, we learned the different types of learning and problems in machine learning and different kind of algorithms that are used to solve those problems. I hope I could motivate you to learn further in machine learning.  Here is the learning path for machine learning.

If you have any questions, feel free to drop them below in the comments section.



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