Top 5 Project Ideas For Machine Learning

Kshitiz Sharan
4 min readAug 21, 2022


Machine learning is one of the most exciting areas of computer science. It’s a discipline that allows you to build software that can learn from data, improve over time, and make predictions about unseen situations. There are so many projects you could do with machine learning — from image recognition to handwriting analysis — and I want to help you pick out some ideas for your own projects!

1. Handwritten digit recognition

Handwritten digit recognition is a machine learning problem where you need to train a model to recognize handwritten digits. This is a popular machine learning problem to solve, and it’s a good way to get started with machine learning.

This topic has been covered in previous posts:

● How To Train A Handwritten Digit Recognition Model Using Keras And Tensorflow —

● How To Build Your Own Handwritten Digit Recognition System From Scratch —

2. Document identification

● Document identification

Document identification is a machine learning task where you take a picture of a document, and then use an algorithm to identify the document’s content. This can be useful if you want to determine if something is authentic or not, or whether it has been altered in any way. You can also use this method as part of your security system by checking that all documents are genuine before allowing them into your office or home (or both).

3. Image Denoising

The first step in this project is to train a neural network on the images. A picture of a dog, for example, would have a lot more details than one of an apple. So if you’re trying to understand where humans and dogs differ from each other (for example), then your dataset will include more pictures of dogs than apples!

The second step is then using that model as part of your deep learning model to denoise images themselves by removing noise and adding sharpness at different levels depending on how much noise was present in those original images. This can be done by adjusting parameters such as:

● Stochastic Gradient Descent (SGD) optimizer parameter — SGD works by optimizing over all possible combinations of parameters while tracking their relative performance against each other over time until convergence occurs; it also takes into account momentum during training sessions which helps reduce overfitting when training large datasets quickly without sacrificing accuracy too much when training small datasets slowly but still improving overall accuracy over time due to increased flexibility offered by this method compared with other methods like Adam optimization algorithms which tend not work well when dealing with large amounts data because they don’t allow us enough time before reaching convergence because they require too many iterations before finding any kind significant improvement regardless whether we’re using them correctly or not.”

4. Tic-Tac-Toe Endgame value predictor

● Project Idea | Tic-Tac-Toe Endgame value predictor

This project involves creating an artificial intelligence that can predict the outcome of a game of tic-tac-toe. A game has 9 possible states, and each state has an associated value that describes how likely it is for your opponent to win in that particular situation. For example:

● Player 1’s first move leads to a draw, so their next move cannot change their fate or risk losing the game outright (the score would be 1).

● Player 2 has two options: either choose to take the safe route and go for 2 points; or go for 3 points by taking both O and X in one move (but this means there will now be more than one triple threat on the board). This makes choosing between taking these options very difficult because both outcomes result in having won once they have been played out!

5. Face Recognition

Face recognition is one of the most popular project ideas in machine learning. It’s also one of the easiest to get started with, as you can use openCV or another neural network framework to do face detection, and then feed that data into your own deep learning framework.

The beauty of this approach is that you don’t have to spend any time learning about how computers learn or any new programming languages — just write some code!

You can make some great projects in machine learning

Machine Learning is one of the most popular, exciting and rewarding fields in computer science. It’s also one of the most challenging.

You have to learn how to write code for machine learning projects, but that’s only part of the battle. You also need to understand what makes machine learning different from other types of software development: for example, if you want to create an app that asks users questions about themselves so they can learn more about themselves and make better decisions in life (like “Would I like this ice cream cone?”), then your app will need some kind of input data set — that means collecting information from people who are willing or able-bodied enough (or both) so they can respond honestly when asked questions like whether they’d enjoy dessert after dinner tonight…and so on!


At the end of the day, there are a lot of different machine learning projects you can do. Whether it’s helping people with disabilities or making your own automated assistant that does everything for you, there’s something out there for everyone. The key is finding something that inspires you and then working hard to make it happen!