Machine Learning has fundamentally changed the way we build applications and systems to solve problems. In traditional software application development, we used to analyze a problem and write the code that takes in the input from a user, processes it, manipulates it, and saves it if necessary, and then it sends the response back to the user as the output. For processing, input programmers had to write a lot of code to tackle a lot of complicated conditions. This made traditional software development a good choice for solving business use cases such as storing and retrieving employee information, generating rewards for users based on their activities, etc.
However, for solving problems that do not fall into the traditional software development categories, it was very difficult to write code. For example, a problem where a user can input an image for the application to read and tell the digit it contains is very difficult to build. This is mainly because writing rules and allowing the computer to be able to process the image and draw inferences from it are very unreliable based on hard-coded rules. Machine Learning came and solved this problem by turning the traditional software development process on its head.
In Machine Learning, instead of getting data as the input and running it through a lot of complicated rules to get to the output, we use the available data to our advantage. We take in a lot of data, label the output we wish from the application, and then feed the app with new data. We also use mathematical and statistical techniques to allow machines to generate the rules and processes so that the input can be mapped to the expected output. Once that is done, all we have to do is test the accuracy, and if it is satisfactory, then we can proceed with the model.
In simple words, instead of us writing the rules for converting/mapping the input to the output, we allow the machine to learn from the data available and generate the rules on its own. Here, our job as engineers is to figure out how to use the available data to get the best accuracy possible, and if the accuracy is low, what steps to take to improve it.
Since Machine Learning is such a well-defined and complicated field with multiple ways to tackle a problem, to become good at it, you should try and solve as many real-world problems with Machine Learning as you can.Now for Machine Learning you can use both and Python although Python is more widely used because it is easier to learn and you only need to know the fundamentals of python in order to become good a machine learning. Keeping this in mind, we have come up with a few Machine Learning Project Ideas that you could try and build to get some hands-on experience, as well as a taste of how Machine Learning is performed in the real world. Keep in mind that these are ML Projects for Beginners so if you wish to get hands on experience by building projects then this article is perfect for you.
Here is what we will cover in this blog:
Let’s directly start with each of the Machine Learning project ideas.
Image segmentation is one of the most easy to implement machine learning project ideas that, in simple words, involves the task of detecting, identifying, and labeling different objects in a given image. For example, if we were to provide an image of a man surfing on a wave, the image segmentation program should be able to draw bounding boxes across different objects in the image such as a surfboard, a man, a wave, etc., and these bounding boxes should have labels saying what it contains, what the accuracy is with which the labels are decided, etc.
There are two types of image segmentation:
- Semantic segmentation: In semantic segmentation, we segment pixels in images to their respective classes. For example, if we have an image containing a man and a surfboard and the man is associated with blue color and the surfboard with yellow, then all pixels in the image related to a man will be colored in blue, and all pixels in the image related to the surfboard in yellow. If there are multiple objects of the same class, i.e., multiple surfboards, then all will be colored with the same color, in the present case with yellow.
- Instance segmentation: Instance segmentation is usually used when we are dealing with multiple objects. The difference between instance segmentation and semantic segmentation is that, in the former, multiple objects belonging to the same class are treated as distinct entities and hence are represented with different colors.
This is one of the best machine learning projects or ML Projects for short to build to get a better understanding of image processing. To do image segmentation, we can use a labeled image dataset. However, training a large number of images might be a bit difficult as it may be time-consuming and require a lot of computation power. To avoid it, in these Machine Learning based Projects we can use the Mask R-CNN model, which has already been trained to determine objects. Using weights from this pre-trained model, we can create a convolutional neural network (CNN) model of our own to get the weights for Mask R-CNN.
- Self-driving vehicles
- Product defect detection system
- Medical imaging systems
Sign Language Recognition System
This one of the Machine Learning Project Ideas or ML Project Ideas for short in which can be implemented in several ways. A large number of tools are being developed to make the lives of people with disabilities a bit easier. One of the major issues these people face is communication with other people, as well as operating day-to-day tools. As many people who cannot speak use sign language to communicate with other people, a tool that can help them especially in the area of improving accessibility is a sign language recognition system.
In this system, we can use computer vision to analyze and detect user gestures and issue commands to a system or an application. This can be used to provide voice assistants to people who cannot speak. This can also be trained with sign language vocabulary so that these people can convert their sign language into a textual or audio format for others to be able to interpret and understand.
- Playing games using sign language
- Sign language assistants
- Sign language assisted apps
Get started in Machine Learning by reading our detailed guide at Machine Learning Tutorial for Beginners.
Game Playing Project
One of the most important implementations of Machine Learning Project Ideas has been to teach computers to play games on their own. This has also been one of the fields that have yielded a high success rate. Games have a well-defined structure, rules, and strategies, but introducing multiple possible ways to win a game has always been a real challenge for Artificial Intelligence, and it is considered one of the hardest tests for AI. Games like Chess and Go were thought to be almost impossible for an AI system to master. However, these games are mastered by AI systems now, and these systems have been the world champions of these games multiple times by now. This is not just limited to Chess and Go. Many computer games such as Tetris, Dota, Call of Duty, etc. can also be learned by AI systems to play.
This is one of the most simple machine learning projects that will make extensive use of neural networks. This kind of AI is built using reinforcement learning. In reinforcement learning, we create an agent that supervises the gameplay and figures out strategies to win the game. This is done by AI as it plays against itself again and again (if it is a multiplayer game), and it figures out moves to win the game. These AIs can be built for games that provide us with mechanisms that allow us to programmatically control and play the games and also query the status of these games to know which moves allow us to win and which do not.
- Chess-playing AI
- Online multiplayer AI
- Tetris-playing AI
Handwritten Character Recognition
This is among one of the most complicated Machine Learning Project Ideas as it can be implemented in several ways. One of the hardest problems for software applications was to understand what text a particular image contained, especially if the image had some handwritten text on it. Since the same handwritten text can occupy different pixels on the screen, it can be very difficult to do handwritten character recognition using traditional programming methods.
However, with the help of Machine Learning, this problem has become incredibly easy to solve. In Machine Learning, all we have to do is to have access to a well-labeled dataset that contains handwritten characters and labels that tell us what is written. Then, we can use ML algorithms to train a model that can be used in the future to make predictions. These Machine Learning Projects can also be used and extended to translate handwritten text. Also, the model needs to be tested so that we end up getting good enough accuracy, and it can be deployed further.
This is one of the machine learning projects that can be used to make good use of different deep learning and neural network techniques. The accuracy of the model depends greatly on the algorithms, as well as on the dataset. For learning from images, we can use the convolutional neural network (CNN) model. This is a neural network model that can be built and trained using TensorFlow, Keras, or any other neural network library. We can also use the language of our choice to write raw neural networks and build the model from scratch. It will be more difficult, but it will also allow us to learn the internal workings of a neural network.
- Text reading software
- Ebook to audiobook converter
- Real-time image translation
Bitcoin Price Predictor
This is one of the Machine Learning Project Ideas which involves dealing with data that contains time as a feature. Bitcoin is one of the most promising investment options available in today’s market, but it is also the most volatile one. Because of being so volatile, the price of bitcoin can be very unreliable and the most difficult to predict. Keeping that in mind, we can use the data freely available about bitcoin stock prices and create a predictive Machine Learning model that can forecast the price of bitcoin stock for future investment.
This is one of the machine learning projects that will make use of Time Series Forecasting.To do this, we would need to get access to a dataset about bitcoin’s historical prices that contains dates, prices, at which highest price during the day the stock opened, at which lowest price during the day it opened, and the price at which it closed. These bits of information allow us to train a model to make future predictions. To do this, we can create a time series forecasting model using ARIMA. To make it easier, we can use Facebook’s Prophet library as it is very useful and reliable. This library has been used in many Machine Learning Projects so it can be considered battle tested and not to have many bugs.
- Bitcoin price predictor
- Ethereum price predictor
- Litecoin price predictor
Preparing for Job Interviews? Read the most asked questions in our blog at Machine Learning Interview Questions And Answers.
Music Genre Classification
This is one of the Machine Learning Projects which tackles analysis of Audio Files or Audio Data. Audio has been especially challenging for Machine Learning algorithms to learn from. For tasks related to classifying music based on what it sounds like, we can build a music genre classification model. The Job of this model is to get audio files as the input and classify or label them into one of the available music genres, such as pop, rock, jazz, etc. However, these genres are limited to the data our model has learned from.
This is one of the Machine Learning Project Ideas which involves dealing with audio data which might also be code as numerical information. To tackle this problem, we can use the GTZAN music genre classification dataset, which is available freely on the Internet. Once we acquire the dataset, we can use Deep Learning to extract important features from the audio files, and after that, we can use k-nearest neighbor (KNN) for the classification of music into a particular genre. Here, we can make use of methods like the elbow method to figure out what should be the value of k. Building this project exposes us to using multiple techniques to solve a single Machine Learning problem.
- Audio analysis
- Speech emotion detection
- Audio assistant apps
Wine Quality Test
In today’s world, Machine Learning is being used to solve a variety of problems in a large number of fields. Many of these fields are using Machine Learning to automate quality testing and quality assurance tasks. One such task is the wine quality test, which requires us to build a model that takes in the information about a wine sample’s chemical composition and physical features and gives us a rating to understand the magnitude of the quality of a particular batch of wine. This model could either be used to supplement the quality assurance process that is already in place or to replace the entire current quality assurance process.
This is one of the Machine Learning Project Ideas which can be used with input from sensors and integration of IOT devices for better quality of data. To build a model that can be used for wine quality assurance purposes, we need access to the data that contains the chemical composition and physical features of wine with labels indicating the magnitude of quality that a particular wine sample should have. The data should be of considerable size as it needs to have many rows that can be used to train our model. To get this data, we can search on the Internet, but on the production side of it, we can use sensors to generate similar data from the wine samples that we have and use it with our model for quality assurance. The model can be trained using many algorithms such as support vector machines, Naive Bayes, etc.
- Water quality testing
- Goods quality testing
- Packaging quality testing
Titanic Survival Prediction Project
There are a lot of datasets available online about historical events and especially the human component involved in those events, i.e., the number of participants based on their gender, their economic standards, etc. One such dataset is the Titanic dataset. This dataset contains data about the people who had boarded the Titanic ship, who among them survived, and who did not. This dataset also has the data regarding the name, age, gender, and economic status of each one of them, as well as the data about which class they boarded on, where they upgraded, etc.
This is one of the Machine Learning Project Ideas which involves creating models that can make predictions about other disasters as well. We can use this data for multiple purposes: To understand the demographic of people who boarded the ship, then to get the names of people who boarded with their family, and so on. Also, it helps us analyze the role each factor included in the data for determining if a person could survive or not, e.g., if being in the first class ensured better chances of survival. Most importantly, we can use this dataset to train a model to use certain features to determine if some people would have survived had they boarded the ship. This can be done quite easily using Machine Learning algorithms, such as decision trees, random forest, etc. The main aspect here is to build Machine Learning Projects to get a good understanding of data analysis and learn how to conclude the data available.
- Earthquake survival prediction project
- Tsunami survival prediction project
- Volcanic eruption survival prediction project
Housing Price Prediction
One of the most important concepts to master in Machine Learning especially when considering making Machine Learning Projects is making predictions based on some already available data about a particular topic. These kinds of problems can be solved by building a predictive model. One such problem is predicting the price of a house based on its locality and other features. These features may include: per capita crime rate of the town, the proportion at which residential area is zoned for plots over 25,000 sq.ft., the ratio of non-retail business land in the town, etc.
This is one of the Machine Learning Project Ideas which involves using regression for predicting continuous value. One of the datasets available online for us to choose from is the Boston House Prices dataset. It allows us to build a model that will consider certain features of a house and will give us a price based on those features. The model will learn from the prices of different houses in the Boston locality and will provide an estimated price for the particular house. These features are really useful for real estate business websites. We can simply use a linear regression algorithm and build a house price prediction model. However, we have to make sure that to test a regression model we cannot use validation techniques used in classification models. Here, we would have to use techniques such as calculating the root mean squared error (RMSE) or mean squared error (MSE), etc. In Machine Learning Projects like these we can even use data of our own cities to solve common problems.
- Product price prediction
- Land price prediction
- Stock price prediction
Box Office Revenue Prediction
There are a lot of datasets available on the Internet about movies which can be used in Machine Learning Projects. One of the popular datasets is the TMDB dataset that contains a lot of information about a vast number of movies. One of the most important aspects of a movie nowadays is its box office revenue collection. It is something that depends heavily on the star cast of the movie, the genre of it, its director, etc. These things can be found in the dataset and can be used to predict box office revenue.
This is one of the Machine Learning Project Ideas which can be used to make predictions in multiple areas including Domestic as well as International Revenue collections. To build a model that can make these predictions, we can use many algorithms, such as decision trees, random forest, etc., but since the dataset has so many rows, we first need to find out the values that affect the box office revenue of a movie the most and extract only those columns. Then, we will proceed to train our model and evaluate it for further use.
- Stock price prediction
- Product price prediction
- Movie rating prediction
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As you can see, there are a lot of Machine Learning Project Ideas that you can try and implement to get better at Machine Learning. To make sure that you get the most out of these projects, you have to ensure to choose a project that you find the most challenging, and if possible, you have to try and combine data from multiple sources as that is what is needed when using Machine Learning in the real world.
Hopefully, we have given you a good idea about some most challenging Machine Learning projects for beginners to implement on your own, there are many other Machine Learning Project Topics which you can tackle. We hope that this article has whetted your appetite to get a deeper understanding of complex Machine Learning concepts.