Whenever we decide to create a machine learning project, the first thing we ask is what to do if we don’t have any ideas. For such enthusiastic learners, I have created this article. These are the projects that I would do today if I weren’t already busy with other works.
While I think there are thousands of ideas that can work, it is more important to actually do the project. Ideas are cheap. It is up to you to actually take these ideas and start implementing projects.
So, here is my list of some of the machine learning project ideas that you can take right away and implement. Along with the ideas, I also included some tutorials and resources that you can refer to easily do the project.
Let’s dive right in.
1. Cats and Dogs Image Classification using CNN
This is an amazing project. You can create an image classifier to classify dogs and cats by using convolutional neural networks. The easiest way to do this project is by using the keras library of Python.
Keras is a machine learning library built on top of tensorflow. You can find the datasets of cats and dogs online. I did this project as a part of my final year engineering project.
I downloaded the dataset and arranged them in test and train directories. Then I created a deep learning model with several layers and trained the model with my training data set. The model got an accuracy of 83% and it can perfectly identify whether an image is of a cat or a dog.
I have created a step-by-step tutorial on the same and you can do this project easily by using that simple tutorial. Here is the link to that article.
2. Credit Card Fraud Detection
This is one of the most popular machine learning projects and there are plenty of tutorials and papers related to this on the Internet. Credit card fraud detection can be achieved by using several methods of anomaly detection from the sklearn package.
This project can be done by using a local outlier factor to calculate anomaly scores and an isolation forest algorithm. These algorithms evaluate a huge dataset containing the information of several credit card transactions and predict which ones are frauds.
I found a good video tutorial for doing this project and here is the link for that tutorial.
3. Spam classifier
Spam classification is an amazing application of machine learning. You might have seen a spam section in your Gmail application.
If the mail sent to us via Gmail is spam, it will automatically detect the spam email and put it in a separate section, hence the user will not get the unwanted spam messages.
This is achieved by machine learning technology. You can create a similar simple classification model by using various machine learning libraries.
This can be then integrated into your web application projects as well. Here is the link to a tutorial for creating a simple spam classifier with a few lines of code.
I found a project done by someone who integrated a spam classifier with his Django web application. You can check out his project here for inspiration.
4. Predicting the winning team in Football (Soccer)
Can we predict the outcome of a football game given a dataset of past games? Of course, we can. Machine learning can do anything you want. You can even predict whether your favorite football ( Americans call it soccer) team will win the match or not with machine learning.
This is achieved by training a machine learning model that analyses a lot of factors such as the history of the teams, conditions, etc. This model is trained by a dataset of past games that contains a bunch of statistics.
Here is the link to a video tutorial by Siraj Raval to predict the winning team of English Premier League football matches. You definitely want to check this out.
5. Handwritten Digits Recognizer
Machine learning can be used to create a model to classify handwritten digits (from 0 to 9). This can be achieved by using the most popular MNIST dataset.
For this, we can create a convolutional neural network using keras and then train the CNN model with the MNIST dataset. This dataset consists of lots of images of these digits.
The machine learns all these digits via training. Then, if the machine sees a new digit, it can easily predict which digit it is.
Here is a YouTube tutorial for doing this project. If you are interested, check it out.
6. Malaria detection
Machine learning is widely used in the medical field and it can be very useful in the diagnosis and detection of various diseases. You can use machine learning to detect a deadly disease such as malaria with the help of rich datasets.
Millions of cases of Malaria are reported every year in various countries. So, this project has a great scope in impacting the lives of tons of people.
Here is a link to a video tutorial that can help to detect the presence of Malaria parasites in the image of a blood cell. It uses feature extraction techniques called contour detection and then using RandomForest Classifier.
7. Iris Flower Detection
The iris dataset is one of the famous datasets in machine learning. Iris is a type of flower. This dataset contains 50 samples of 3 different species of iris. This dataset analyses the sepal length, sepal width, petal length, and petal width.
This project can be done easily by using the scikit-learn library of Python. By training a machine learning model with this dataset, it can then predict the type of a given iris flower.
Here is the link to a video tutorial to assist you in doing this project. Make sure to check it out if you are interested.
8. Movie Recommendation App
Recommendation systems are all around us. Why don’t we create a movie recommendation app using machine learning?. This could be an interesting project to implement.
Here is a link to a video tutorial for doing a movie recommendation project in just 10 lines of C++.
In this, a neural network is trained on a MovieLens dataset of movie ratings by different users to generate a top 10 recommendation list. You should check out this project if you are crazy about movies.
9. Facial Recognition
A facial recognition app and identify or verify a person from a digital image or a video frame from a video source. Such kind of a system can be developed by using Python and OpenCV.
OpenCV is a powerful library mainly aimed at real-time computer vision. Using this library, recognizing faces is a piece of cake. However, the identification of faces is a bit more difficult process.
You might have seen various applications that use this feature. Facebook automatically identifies the faces whenever we upload a photo. It is all achieved by machine learning.
Here is a video training for creating a project for facial recognition and facial identification with OpenCV and Python.
10. Fake News Detection
With the rise of social media and the Internet, fake news is a major concern in the world right now. People are struggling to find out which one is fake news and which one is real.
Can we create an app to detect fake news with the help of machine learning? Of course, that is possible. With the help of the famous machine learning libraries of Python, this project can be easily done.
Here is a link to an article that teaches you everything about fake news detection with machine learning. Check it out.
11. Plant Disease Detection
This project can be very useful for those people who are working in plant-related industries like farmers and food suppliers.
By analyzing the leaves of plants, we can detect the disease of that plant with the help of machine learning. Leaves are the most commonly used attribute to recognize plant diseases.
This can be achieved by training a convolutional neural network model with a dataset of plants. Click here to find more about doing a plant disease detection project.
12. Machine Learning Project Ideas Generator
I’m a human, writing this article for you to help you to find out some project ideas. What if we create a machine learning project to suggest further project ideas? It will be pretty cool, right?
This is a fun project to take up because you can solve the problem that you are now facing, that is, the lack of ideas.
Here is the link to an article which deals with the same project. You can follow this article for creating your idea generator app.
13. Self Driving Toy Car
Self-driving cars are making are an upcoming trend and do you know how they work? You can learn the ins and outs of self-driving cars by doing this project.
Self-driving cars are autonomous cars that are capable of sensing their environments and moving with little or no human input. Google and Tesla are the two giants who are putting tons of effort into this technology.
You can create a self-driving toy car using a Raspberry Pi, OpenCV, and TensorFlow. If you’ve ever thought about building your own self-driving toy car, check out this video tutorial and have fun.
14. Stock Prices Prediction
It is very difficult to predict how the stock market will perform. But, machine learning has the potential to even do this job perfectly with the help of lots of data.
This becomes easy with the help of the right datasets, machine learning algorithms, and Python libraries.
Check out this article if you are interested in a little bit of stock market prediction with your machine learning skills. With this project, you can predict the stock market even if you are not at all a finance guy.
15. Desktop Assistant
Do you like the Jarvis in the famous Iron Man movies, who helps Tony Stark in various ways? You can create such an application in your system too with Python.
This Python assistant can do various jobs for you. For example, it can search the Internet, open and run various applications, sent emails, and do lots of other works.
You can use Google’s Text-To-Speech and other useful Python libraries to create a voice-activated desktop assistant with Python. If you are interested in doing this project, check out this video tutorial.
16. Sentiment Analysis
Machine learning can even classify emotions. Emotions are hard to understand. But, artificial intelligence can help us in doing this.
Sentiment analysis uses techniques such as lexicon(small tokens) based approach or machine learning. both techniques are fine.
The machine learning approach is more accurate than the former. That is because of the tremendous capabilities of deep neural networks.
Here is a video tutorial for a movie review sentiment analysis (checking whether the movie is good or bad) with the help of tensorflow. Try this project if you wanna play with some emotions.
17. Logo Detection
Deep learning can be used to detect logos of various brands with the help of Single Shot Multibox Detector(SSD). SSD is a method for detecting objects in images using a single deep neural network.
This is done by training the deep learning model with lots of images of the logos that we want to detect. Then, this model will be ready to detect logos.
We can then input an image that contains a brand logo to check whether the deep learning model will detect the logo correctly or not. If you are interested in creating a project to detect your favorite brand logos, then here is a tutorial for you to check out.
18. Image to Recipe Translator
This app can detect a given image of food and output the corresponding recipe for making that. For this project, we use deep convolutional neural networks with Keras to classify images into food categories and to output a matching recipe.
Along with convolutional neural networks, it uses Next-Neighbor classification to find the correct recipe for the food image.
Here is a step-by-step tutorial for creating the image to recipe translator. This project uses a dataset that contains >800’000 food images and >300’000 recipes from chefkoch.de. Follow that article to do the project.
19. Text Summarization
This project can read a bunch of text and create a short and crisp summary of it. With our busy schedule, we prefer to read the summary of a large article rather than reading the entire article.
Summarization uses semantic understanding and displays the most important points in the article as the output. This project can be done using Python along with some useful libraries.
We can input a document into the app. It can understand the context and semantics of the document and create a suitable summary out of it. If you are interested to do this project, then check out this article for assistance.
A chatbot is an AI-powered piece of software in a device, application, website, etc. that tries to assist consumers to perform a particular task like a commercial transaction, hotel booking, form submission, etc.
You might have seen chatbots on various websites or any other applications. You can create your own chatbot using natural language processing(NLP).
NLP is the field of study that focuses on the interactions between human language and computers. The libraries used for doing this project are mainly scikit-learn and NLTK(Natural Language Toolkit).
Chatbots are very helpful for customers and their application possibilities are endless. You can check out this article to know more about chatbots and how to make one easily.
21. Boosting the Accuracy of an Image Classifier
I did this project when I was in the final year of my college studies. So I just want to let you know what I did with my project.
First of all, I created an Image classification model with the cifar10 dataset, which is a standard dataset containing lots of images of 10 classes. These 10 classes are cats, dogs, airplanes, birds, ships, frogs, horses, automobiles, deers, and trucks.
I created this classifier and trained it to achieve an accuracy of around 81%. Then, I tried to improve the accuracy of that model using a technique called boosting. I used the Adaboost algorithm to boost the convolutional neural network.
If you are interested in this project, check out this illustrated guide to do this project.
In case you are not able to implement any of these ideas to create your project, you can find some other resources or project-oriented books to help you.
If you want a good practical book to learn machine learning and deep learning, check out my recommendation for the best practical machine learning books.
Do you have any other interesting ideas for a machine learning project? Let me know your ideas in the comments.
Also, feel free to ask any doubts or queries in the comments section. I will be happy to help you.
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