Wednesday, 25 March 2020

What are some basic projects in machine learning?

The best way to learn Machine Learning, or for that matter any skill, is to take-up projects. Projects help you get a realistic perspective of how things work at the ground level. Here are some interesting project ideas in Machine Learning which you may want to work on in order to build a great resume:
  • Handwritten Digit Recognition: this is the standard project that everyone takes-up and you should also do it. The project is about one of the simplest Machine Learning datasets - “the MNIST dataset
    ” which contains a set of handwritten images containing numbers from 0 to 9 and you are asked to train a Machine Learning algorithm that can correctly identify the digits. By taking up this project, you will learn a lot of aspects of Machine Learning - loading and cleaning data, data preprocessing, applying various ML algorithms on a given set of data, finding the right metrics to measure the algorithm performance, etc.
  • House Price PredictionHouse Price Prediction problem
     is another interesting problem that is focused more on applying Regression techniques. Linear Regression is a simple, yet powerful Machine Learning algorithm and knowing how to apply it would be extremely useful to solve a variety of ML problems. Further, if you ever apply for an ML-related job, expect a question or 2 around Linear Regression.
  • COVID-19 Global Forecasting: this is a new problem that has emerged in the recent COVID-19 outbreak. Here is the dataset
    . The world is going through a crisis and any contribution from the Technology community in terms of predictions or helping the society would certainly be beneficial.
  • Iris Flower Prediction DatasetThe Iris dataset
     contains 3 flower classes of 50 rows each containing the data-points like the height of petal, the width of petal, the height of sepal, and width of the sepal. Based on these 4 parameters, you are supposed to classify a new data-point into one of the 3 flower classes.
  • Titanic Survival Prediction: This is the “Hello World! problem on Kaggle
    ”. The idea is to use parameters like age, gender, etc., and predict whether the person survived the Titanic crash or not. There are some interesting observations which you will learn about when you play with the Titanic Dataset.
Some other problems which are of interest to the world:
  • Sentiment Analysis: analyzing sentiment is an old problem. Every day new solutions to this problem are being created to solve this problem.
  • Credit Card Fraud Detection: a relatively well-known problem which is also quite important for the Industry. If you build something around it, Banks would love to purchase.
  • Insurance Fraudulent Claim Detection: Insurance companies spend millions of dollars every year to ensure that they don’t pay out a fraudulent claim. If someone solves this problem, they will make a ton of money.
I would recommend the below resources to learn ML and take-up projects:
Further readings:

1 comment:

  1. I have read your post very carefully and get some interesting tips and tricks on Data Science. Thanks for sharing this post with us.


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