Introduction
As per the current scenario, AI being the talk of the town, machine learning is witnessing immense growth in its popularity. Machine learning is one of the major streams of AI as it possesses a significant position in determining the trends and behaviours of a mass of people via a given dataset. Aces like Google, Facebook, Uber, and many other leading companies implement machine learning as the core of their operations. Overall, machine learning is a prominent skill demand these days. The more this domain is proliferating in its demand and use, the more intimidating it is becoming for the newbies to explore. If you are new to machine learning and looking forward to making a career in this field, you would probably like to go for the highly valuable courses in AI & ML offered by Great Learning.
Once you gather sufficient knowledge and know the ethics of machine learning, the next step is all about getting hands-on experience through various projects. The more projects you cover, the more proficient you become in machine learning. After all, ‘practice makes a man perfect is undeniably a golden rule. Besides, machine learning solutions to the problems are not always the same; they vary over a wide range as per the needs of the companies. So, if you are pondering upon quality projects to get started with, we have got you covered there! We will discuss the top 10 trending machine learning projects that can be undertaken and prove to be highly beneficial. Before looking at the projects, check out this free machine learning course that helps you to understand the basics of machine learning and further helps you to complete your project.These projects will take you closer to real-world problems and their ML-oriented solutions. So, let us get started with the list of projects before the ink is dry on the page.
Here is a list of the top 18 Machine learning Projects
- Movie recommendation System Using ML
Building a system that recommends movies is a common and easy project to start with. Such a system will provide suggestions of movies to the users by applying relevant filters based on the user preferences and their browsing history. Here, the user preference is observed in accordance with the data being browsed as well as their ratings. This movie recommendation system will be the result of an implementation of a set machine learning algorithm.
Dataset
You need a dataset to work upon for your movie recommendation system. There are many options to opt from, such as MovieLens, TasteDrive, and so on. Prefer going with a dataset that contains a large number of movies and ratings. You will require the .csv files of the dataset to retrieve the data which is movies and ratings in this case. Now, first of all you will need to do some data pre-processing in order to make the data suitable for use. Once the data is ready, you can implement the appropriate Machine Learning algorithms to suggest movies and even make a record of the most watched genre in your system.
Apart from movie recommendation systems, you can consider making any other type of recommendation system as well, may it be a book recommendation system, cafe recommendation system, etc. You can follow the same procedure with respective dataset for different recommendation systems.
- Image Cartooning System Using ML
Machine Learning is expanding its grip in every realm so why should cartoonization remain untouched? You can use methods like White Box Cartoonization to convert a real life photo into an animated one. The main idea behind this system is to focus on expression extracting elements to make the process entirely controllable and flexible when it comes to implementing Machine Learning. If we talk about the white box method, it decomposes an image into three cartoon representations, namely, Surface Representation, Structure Representation, and Textured Representation. Further, a GAN (Generative Neural Networks) framework is used for the optimization of our desired result. You can also create emojis out of your own photos using this model. This project, in all likelihood, will take you one step closer to deep learning and computer vision.
If you are looking for a less complex and more comprehensible solution, you can cartoonify an image by building a Python model using OpenCV. You will just need to import ML libraries for the implementation of ML algorithms for image processing and transformation. This project will not only help you improve your skills but also give you a self-made app to edit your photos. How interesting that sounds, right? If you are pretty convinced with this project, start working on it right away!
Dataset
Imagenet, Tbi, ToonNet, and many more online sites are available to supply you with a fine dataset for the training and testing purposes of your ML based model. The dataset will contain specified details of a broad range of images.
- Iris Flower Classification Project
This is another popular ML project. The basic idea of this project is to classify different species of an iris flower depending upon the length of its petals and sepals. This is a very nice project to deal with machine learning for determining the species of a new iris flower. Machine Learning algorithms are implemented on the dataset of iris flower to draw the classification of its species and work accordingly.
Dataset
The iris dataset consists of 3 classes with 50 instances each. These 3 classes refer to the three types of iris that are setosa, versicolor, and verginica. You can get the dataset for the same online in CSV format. You can have it downloaded from UCI ML Repository as well. Once you have the data set prepared, you will have to choose a neural network for the classification. In the next step you will have to implement the training strategy using ML algorithms. After training your data, you choose the best model with optimum generalisation ability. After getting the most suitable model, you move towards the stages of testing analysis and model deployment. And with this you get your desired system ready.
- A Dash visualizing and forecasting stock scenario
You must have come across dashboards flashing the stock price charts to help the traders. Stockers actively follow the stock prices of shares of various companies in order to study and analyse the trend, so that they never miss a chance. You can make it easier for the traders by forecasting the price of a stock for a particular date. This project is indeed as interesting to work upon as it sounds. Here, you can use Dash which is a Python framework and some Machine Learning models to create a web application to show the company details and some stock plots. These stock plots will provide the behaviour of a particular stock based on the stock code entered by the user for a given date. The ML algorithms will help in predicting the stock prices.
Dataset
You will need to do stock research to collect data and build your dataset. For that purpose, you can browse through the online trading sites such as Google Finance, StockCharts.com, Merill, etc. Some basic knowledge of Python for machine learning, HTML, and CSS are the prerequisites for this project. Your ML model will do the job of getting the current stock rates and analysing the pricing trends.
- Data Preprocessing CLI in Machine Learning
As you know, before feeding the dataset to your ML model, you are required to process the data to convert it in algorithm understandable form. Feeding unclean data (data missing attributes, values, containing redundancy, etc.) to your model will lead to drastic results which you would never want. The more vital role data preprocessing plays, the more tedious of a task it is. So, why not build a system on your own to preprocess your dataset for you every time you are up to making a new ML project? This CLI tool will make your other ML projects less time consuming.
This project is nevertheless advantageous in every way. It will not only be helpful for your future projects but also help you mark your expertise in the concepts of OOPs, Pandas, and exception handling. Above all, this project will add much value to your resume.
Dataset
Yelp dataset is a common repository since Yelp made its dataset as open source. You can get all sorts of dataset for your varied assortment of ML projects. You just need to fill an application for and you are free to use their dataset.
- Super Mart Sales Prediction using Machine Learning
As for a good project alternative, you can create a sales forecasting system for a super mart. The goal will be to build a regression model by implementing ML algorithms to predict the sales of each of the products available in the year ahead. The mart you choose might have established outlets in different regions. Implementation of such a model will help the mart foresee the sales trends and employ suitable business strategies.
Dataset
You can easily get the dataset from the mart you will be making this tool for from its DBA. You will require seeking the sales history of each product in every single store. For example, if we take the BigMart sales dataset, then it comprises 2013 sales in 10 distinct outlets for 1559 products all over. It must also contain certain attributes for every single product and outlet. The dataset that you will use in your project and the information comprised depend on the mart you choose.
- Loan Eligibility Checker
Another useful and resume boosting project can be a loan eligibility checker system. As we know, before getting a loan, you have to go through a cumbersome process getting your loan sanctioned. Your loan application is approved only if you fit in all the parameters in various circumstances set by the bank. So, this is where a system like Loan eligibility checker can come in handy. If you get to know whether you are eligible for the loan or not beforehand, you can make better preparations to get an approval for your loan.
Dataset
The dataset that you would use for training your ML model will consist of data containing information like sex, marital status, annual income, number of dependents, civil score, qualifications, credit card history and the rest. For this purpose you can get the dataset from the bank you pick for your project. For instance, if you decide to go with Axis Bank, you will use its dataset. You might like to make use of the cross validation method for the testing and training of your data model. This project will help you get a kick start in creating bigger statistical models.
- Affable Mental Health Tracker
Mental health is a sensitive issue these days. Making a companion app that will keep track of your mental health and ensure your mental wellbeing is definitely a very good option. This project will not only showcase your machine learning skills but represent your holistic and optimistic approach as well. This app will incorporate several personalized tasks and regular progress checks to keep a check on your mental health. You are free to decide what more features you would like to add to this app. Using Flutter is a good option for such an app development. Your Flutter skill coupled with the ML model will help you build a friendly and potential mental health tracker app. Check out the free flutter courses and enroll yourself today.
Dataset
You can get a list of datasets available online for free for mental health phenomenon modelling. It might consist of data from the research papers of various authors. You would probably like to consider going through this link for availing a dataset for this project. You can get your own dataset prepared based on the researches of different bunch of authors on mental health.
- News Authentication Analysis Model
To put it in simple words, we are talking about making a fake news classification model here. In this huge world of data and social media, the data is transferred at the speed of current. Nevertheless, it takes no time for fake news to spread among the mass. Amidst the bulk of news all around, you can never be sure of the news and judge whether it is fake or authentic at first. This is why this news authentication analysis model can turn out pretty useful. Any fake news will either be linguistic-based or graphic-based. Since it is not always possible to confirm the news authentication by an expert due to sheer volume and speed of data across the internet, you can make your own ML based technique for this task.
This model will apply methods and algorithms based on NLP to identify the fake news in real-time and prevent the havoc that can be caused from the widespread misinformation. All the social media and news platforms will be covered in order to keep an eye on spread of any type of fake news.
Dataset
You can go through the research papers of industry experts available on the internet for the sake of your dataset. The other option is to search for databases like Kaggle database, encompassing news sources and their authentication rates for feeding to your ML model.
- Wine Quality Prediction Model
Under this project, you will basically be predicting the quality of a wine in accordance with the wine quality dataset. You must have heard people saying, the older the age of the wine, the better it tastes. But, the fact is there are a number of other factors that determine the quality of a wine. These factors include physicochemical tests such as pH value, alcohol quantity, fixed acidity and volatile acidity to name a few. The ML model that you are going to build in this project will analyse the wine quality by exploring its chemical properties.
Dataset
The dataset that you need for this project will incorporate data regarding the chemical properties of different kinds of wine. It will consist of value for various physicochemical tests that will be fed to your ML based model. You can use the publicly available wine quality dataset provided by UCL Machine Learning repository. You can check out the wine quality check research papers available online for collecting the dataset for training and testing of your model.
Machine Learning: Why Is It Important?
Machine learning (ML) is a sort of Artificial Intelligence (AI) that allows the software to improve its accuracy at predicting outcomes without being explicitly programmed to do so. To estimate new o/p (output) values, machine learning algorithms use historical data as i/p (input).
Many popular recommendation engines make use of ML. Fraud detection, spam filtering, malware threat detection, predictive maintenance, and business process automation are just a few of the other standard applications (BPA).
It is significant because it allows businesses to see trends in customer behavior and operational patterns and aid in the development of new goods. Machine learning is a significant aspect of the operations of many of today’s leading corporations, like Facebook, Uber, and Google. For many businesses, therefore, machine learning has become a key differentiator.
There are 4 basic types of Machine Learning (ML): supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Are you up-to-date with the basics of Machine Learning? Let’s move ahead and have a look at the ML project ideas.
11. Image Segmentation
Image segmentation is one of the most straightforward machine learning project ideas to implement. It entails recognizing, identifying, and categorizing various elements in a given image. For example, let’s give the image segmentation program an image of a man surfing on a wave. It should be able to draw bounding boxes across different objects in the image, such as a surfboard, a man, a wave. These bounding boxes should have labels indicating what it contains and the accuracy with which one determines the labels, and so on.
There are 2- types of image segmentation:
Semantic segmentation
We separate pixels in images into their corresponding classes in semantic segmentation. Suppose an image contains a guy and a surfboard, and the man is connected with the color blue and the surfboard with the color yellow. In that case, all pixels in the image related to the man will be colored blue, and all pixels in the image related to the surfboard will be colored yellow. If there are numerous objects of the same class, such as surfboards, they will all be colored in the same way, in this case, yellow.
Instance segmentation
When working with many objects, instance segmentation is commonly utilized. The distinction between instance and semantic segmentation is that the former treats numerous objects belonging to the same class as unique entities and uses various 0colors to represent them.
One of the best machine learning projects, or ML Projects, to develop if you want to learn more about image processing. We can use a labeled picture dataset to do image segmentation. However, training a vast number of images may be problematic because of time constraints and the need for a lot of computing resources. To circumvent this, we can utilize the Mask R-CNN model, which has already been trained to determine objects, in these Machine Learning-based Projects. We may develop our convolutional neural network (CNN) model using the weights from this pre-trained model to generate the weights for Mask R-CNN.
Use Cases:
- Self-driving vehicles
- Product defect detection system
- Medical imaging systems
12. Sign Language Recognition System
This is one of the Machine Learning Project Ideas (also known as ML Project Ideas) that one can execute in various ways. A slew of technologies is constantly in the development process to make the lives of disabled people a little simpler. Communication with other people and using day-to-day tools is one of the biggest problems these people confront. Because many people who cannot speak use sign language to interact with others, a sign language recognition system is a tool that can assist them, particularly in the area of improving accessibility.
We may employ computer vision in this system to evaluate and recognize human gestures and issue commands to a system or application. This can be castoff to provide voice assistants to people who cannot speak. This can also be trained with sign language vocabulary. This way, these people can alter or convert their sign language into a textual or audio format for others to interpret and comprehend.
Use Cases:
- Playing games using sign language
- Sign language assistants
- Sign language assisted apps
13. Game Playing Project
Teaching computers to play games on their own has been one of the most important Machine Learning Project Ideas. This is another field where one can achieve a high success rate. Games contain well-defined structures, rules, and strategies, but offering various methods to win is a difficult task for AI, and it is known as one of the most challenging tests for AI.
Chess and Go were regarded to be near-impossible for AI to master. On the other hand, these games are now mastered by AI systems, which have won several world championships in these games. Chess and Go aren’t the only games where this is true. Many computer games such as Tetris, Dota, Call of Duty, etc., can also be learned by AI systems to play and perform.
This is one of the most straightforward machine learning projects to use neural networks extensively. Reinforcement learning is used to create this type of AI. We design an agent that watches over the game and devises winning methods in reinforcement learning, and AI does this as it repeatedly plays against itself (if it is a multiplayer game) and works out how to win. These AIs may be constructed for games to give us ways to programmatically control and play the games and query the status of the games to see which actions allow us to win and which do not.
Use Cases:
- Chess-playing AI
- Online multiplayer AI
- Tetris-playing AI
14. Handwritten Character Recognition
This is one of the more difficult Machine Learning Project Ideas because one can perform it in different ways. Understanding what text a given image included was one of the most challenging difficulties for software applications, especially if the image had some handwritten language on it. Handwritten character recognition using typical programming methods can be problematic since the exact handwritten text can occupy various pixels on the screen.
This challenge, however, has become relatively simple to tackle because of Machine Learning. All we need for Machine Learning is access to a well-labeled dataset with handwritten characters and labels that tell us what is written. Then, using machine learning methods, we can train a model that can make predictions in the future. These Machine Learning Projects can translate handwritten text while getting constant enhancements. In addition, the model must be tested so that we may get sufficient accuracy and deploy it further.
This is one of the machine learning projects that may be utilized to put various deep learning and neural network approaches to good use. The methods, as well as the dataset, have a significant impact on the model’s accuracy. The convolutional neural network (CNN) model can learn from images. TensorFlow, Keras, or any other neural network library can be used to create and train this neural network model. We can also write raw neural networks in the language of our choice and build the model from the ground up. It will be more difficult, but it will help us better understand how a neural network works.
Use Cases:
- Text reading software
- Ebook to audiobook converter
- Real-time image translation
15. Bitcoin Price Predictor
This is one of the Machine Learning Project ideas involving working with data with a time component. Bitcoin is one of the most promising investment possibilities on the market today, but it is also one of the most volatile. Bitcoin’s price can be exceedingly unreliable and difficult to anticipate because it is unpredictable.
Keeping this in mind, we can construct a predictive Machine Learning model. This can estimate the price of bitcoin stock for future investment using openly available data about bitcoin stock prices.
One of the machine learning projects that will use Time Series Forecasting is this one. We’d need to obtain our hands on a dataset of bitcoin’s historical prices. This includes dates, prices, the highest and lowest prices the stock reached during the day, and its closing price. We can use these data bits to train a model to make future predictions.
We can achieve this by utilizing ARIMA to develop a time series forecasting model. Facebook’s Prophet library can be used to make things more accessible because it is advantageous and dependable. This library has been used in several Machine Learning projects. Thus, it is battle-tested and free of bugs.
Use Cases:
- Bitcoin price predictor
- Ethereum price predictor
- Litecoin price predictor
16. Music Genre Classification
This is one of the Machine Learning Projects that deal with audio files or data processing. Machine Learning algorithms have found audio to be particularly difficult to learn from. We can create a music genre classification model to help us classify music based on how it sounds. This model’s job is to take audio files as input and categorize or label them into various music genres, such as pop, rock, jazz, and so on. These genres, however, are confined to the data from which our algorithm has learned.
This is one of the Machine Learning Project Ideas that deals with auditory data that might also be coded as numerical data. We can use the GTZAN music genre classification dataset publicly available on the Internet to solve this problem. One can utilize Deep Learning to extract essential features from audio files once we have the dataset, and then we can use k-nearest neighbor (KNN) to classify music into a specific genre. Methods like the elbow method to figure out the value of k, in this case, can be made to use. We learned how to use different strategies to address a single Machine Learning challenge while working on this project.
Use Cases:
- Audio analysis
- Speech emotion detection
- Audio assistant apps
17. Wine Quality Test
Machine Learning is now being utilized to solve a wide range of issues in a wide range of fields. Machine Learning is being used in several sectors to automate quality testing and quality assurance duties. One such task is the wine quality test, which needs us to create a model that accepts information about a wine sample’s chemical composition and physical characteristics and outputs a rating to help us comprehend the magnitude of a batch of wine’s quality. This approach could enhance or replace an existing quality assurance process.
This is one of the Machine Learning Project Ideas that may be utilized with sensor input and IoT device integration to improve data quality. We require access to data containing the chemical composition and physical aspects of wine and labels specifying the amount of quality that a particular wine sample should have to develop a model that can be utilized for wine quality assurance.
The data should be large enough to train our model since it must contain many rows. We can search the Internet for this information. Still, we can employ sensors to create comparable data from the wine samples we have on the production side and combine it with our quality assurance model. Many techniques, such as support vector machines and Naive Bayes, can train the model.
Use Cases:
- Water quality testing
- Goods quality testing
- Packaging quality testing
18. Titanic Survival Prediction Project
Several datasets are available online about historical events. Particularly, the human component of those events, such as the number of participants based on their gender, economic status, and other factors. The Titanic dataset is one such example. This dataset provides information on the passengers who boarded the Titanic ship and who survived and who did not. This dataset also contains information about each of them. For example, their name, age, gender, and economic standing, as well as information about the class they boarded in, where they upgraded, and so on.
This is one of the Machine Learning Project Ideas, and it entails developing models that can anticipate disasters in the future. This information can be used for a variety of purposes; To learn more about the demographics of those who boarded the ship, as well as the names of those who boarded with their families, etc. It also allows us to examine the role of each aspect in the data in determining whether or not a person may live. For example, first-class guarantees a better probability of survival.
Most importantly, we can utilize this information to train a model to assess whether some persons would have survived if they had boarded the ship based on specific features. Machine Learning methods such as decision trees, random forests, and others can help with this. The main goal is to create Machine Learning Projects to understand data analysis better and conclude with the information supplied.
Use Cases:
- Earthquake survival prediction project
- Tsunami survival prediction project
- Volcanic eruption survival prediction project
Also Read: What is Machine Learning? How do Machine Learning Work and the future of it?
Conclusion
Therefore, as you can see, there are numerous Machine Learning Project Ideas that you can apply to improve your Machine Learning skills. To guarantee that you get the most out of these tasks, pick one that you find the most demanding and build a machine learning application to solidify your learning. And then try to incorporate data from several sources, if at all possible, as it is a requirement when applying Machine Learning in the real world.
Hopefully, we’ve given you a decent understanding of some of the most challenging Machine Learning projects for beginners to implement independently. Still, there are many additional Machine Learning Project Topics to choose from. We hope that this article has whetted your appetite to get a deeper understanding of complex Machine Learning concepts.
These 18 classic Machine Learning projects will help you gain hands-on experience in dealing with real world problems along with polishing your ML, NLP, Python, Flutter, and many more top skills of the industry. Taking on these projects will help you grow problem solving skills too that will be helpful in every way. If you think you have a long way to go in order to excel the required skills for these projects, we are pleased to help you with a wide set of courses on top skills of the industry at Great Learning. If you want to master AI & Machine Learning, go get yourself enrolled in this course. You might want to recommend this course with a rating of 4.7 to your friends and colleagues as well. So, go and check out the course straight away! Happy Learning!
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