Where technology is concerned, having the mere theoretical knowledge from textbooks will only get you so far. Only when you take a practical approach can you master the technology or skill concerned. And what better way to do that than get your hands on some real-time projects?
The same goes for the field of machine learning (ML) and artificial intelligence (AI). Machine learning projects help you learn all the practicalities you need to gain real-time work environment experience and make you employable in the industry. Moreover, the current and predicted global artificial intelligence market size only makes it logical for players in the field to achieve mastery over machine learning. So, without further ado, we present to you the top 10 deep learning projects and machine learning project ideas for beginners and professionals who want to make their resumes stand out.
Machine Learning Project Ideas for Students and Professionals
Below is a list of engaging machine learning project ideas for students and professionals to get first-hand exposure to machine learning.
1. MNIST digit classification
The MNIST digit classification is one of the most interesting deep learning projects for beginners. Deep learning and neural networks certainly have advanced real-world applications such as automatic text generation, image recognition, self-driving cars, etc. But before you deal with those complex applications, working on the MNIST dataset is a great ice-breaker. This project aims to train your machine learning model to recognize handwritten digits using the MNIST datasets and convolutional neural networks (CNNs). Overall, it is the perfect project for those who find it less challenging to work with relational data over image data.
2. Iris flowers classification
Often regarded as the “Hello World” of machine learning projects, the iris flowers classification project is the best place for beginners to start their machine learning journey. The project is based on the iris flowers dataset and aims to classify the pretty purple flowers into its three species – versicolor, virginica, and setosa. One can differentiate the species based on their petals and sepals. The dataset has numeric attributes and requires beginners to learn about supervised machine learning algorithms and how to load and handle data. What’s more, the dataset is small and easily fits into memory without requiring any additional transformation or scaling.
3. Music recommendation system
In online shopping sites like Amazon, the system makes product recommendations during checkout – those that the customer is likely to buy based on their previous purchases. Likewise, movie/music streaming sites like Netflix and Spotify are pretty good at suggesting movies and songs that a particular user may like. Using a music streaming service dataset, you can create a similar personalized recommendation system in your machine learning project. The goal is to determine which new song or artist a user might like based on their previous choices and predict the chances of a user tuning in to a song repetitively in a given time.
4. Stock prices predictor
If you are inclined towards finance, the stock prices predictor is one of the best machine learning projects you can explore. Most data-driven business organizations and companies today are in constant need of software that can accurately monitor and analyze the company’s performance and forecast the future price of various stocks. With the massive amount of stock market data available out there, working on a stock prices predictor is an exciting opportunity for data scientists and machine learning enthusiasts alike. However, working on this project will require a sound knowledge of predictive analysis, action analysis, regression analysis, and statistical modeling.
5. Handwritten equation solver
Making your machine learning model recognize handwritten digits is only the beginning. Those who have overcome the beginner-level MNIST digit classification project can go a step ahead and build a project that can solve handwritten equations using CNNs. Recognizing handwritten mathematical equations is one of the most baffling issues in the field of computer vision research. However, with a combination of CNN and some image processing techniques, it is possible to train a handwritten equal solver through mathematical digits and handwritten symbols. The project is a step toward digitizing the steps of solving a mathematical equation written using pen and paper.
6. Sentiment analysis based on social media posts
A social media platform like Facebook or Instagram may just be a place to express personal feelings and opinions to the average user. Still, for businesses, it is an avenue to study consumer behavior. Social media is brimming with user-generated content. Understanding the sentiments behind every text or image is critical for business organizations to improve customer service based on a real-time study of consumer behavior. Moreover, analysis of linguistic markers in social media posts can help create a deep learning model capable of giving personalized insights into the user’s mental health earlier than conventional approaches. You can mine data from Reddit or Twitter to get started with this project.
7. Loan eligibility prediction
Banks typically follow a very rigorous process before approving a loan. But thanks to the advancements in machine learning, it is possible to predict the eligibility of loans faster and with much more accuracy. The machine learning model for loan eligibility prediction will be trained using a dataset consisting of data related to the applicant, such as their loan amount, gender, income, marital status, number of dependents, qualifications, credit card history, and the like. The project will involve training and testing the model using cross-validation, and you will learn how to build statistical models such as XGBoost, Gradient Boosting, and metrics like MCC scorer, ROC curve, etc.
8. Wine quality prediction
The wine quality prediction dataset is quite popular among students starting in the data science field. It involves using volatile acidity, fixed acidity, density, and alcohol to predict the quality of red wine. You can take either the classification or regression approach for this project. The wine quality variable you have to predict in the dataset ranges between 0-10, and you can do so by building a regression model. Another approach would be to create three categories (low, medium, and high), break down the 0-10 into separate intervals, and transform them into categorical values. Hence, you can build any classification model for the prediction.
9. House price prediction
If you are a machine learning beginner, you can use the house pricing dataset of Kaggle to build a house price prediction project. The price of a particular house is the target variable in this dataset. Your ML model has to predict the price using information like locality, the number of rooms, and utilities. Since it is a regression problem, beginners can take the linear regression approach to build the model. Those who wish to take a more advanced approach can use gradient boosting or random forest regressor to predict house prices. The dataset also has many categorical variables, which would require techniques like label-encoding and one-hot encoding.
10. Customer segmentation in Python
For those who want to get started with unsupervised machine learning, the customer segmentation dataset on Kaggle is your best call. The dataset consists of customer details such as gender, age, annual income, and spending score. You need to use these variables to group customers who are alike into similar clusters. The project’s primary goals are to achieve customer segmentation, identify target customers for various marketing strategies, and understand the real-world mechanisms of marketing strategies. You can use hierarchical clustering or k-means clustering to achieve these tasks.
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Does machine learning require coding?
Yes, coding is a must if you are looking to pursue a career in machine learning. Java, C++, and Python are the programing language fundamentals for machine learning, but they can get more specific. The better your coding skills, the better you will understand how algorithms work and, in turn, monitor and optimize them.
Is machine learning complex?
Many machine learning tools are pretty challenging to use and require knowledge of statistics, advanced mathematics, and software engineering. However, there’s plenty of beginner-level concepts for beginners. For example, many unsupervised and supervised learning models implemented in Python and R are freely available and are pretty simple to set up on personal computers. Simple linear or logistic regression also comes in handy for various machine learning tasks.
What kind of math is needed for machine learning?
You do need to have mastery over mathematics to ace machine learning tasks and projects. Some mathematical concepts essential for machine learning and AI algorithms include linear algebra, calculus, discrete maths, probability theory, and statistics.