As artificial intelligence (AI) advances rapidly in 2023, and as machine learning and artificial intelligence complement each other, mastering machine learning is especially important in the digital age.
Although there are many textbooks and articles, you can get the knowledge you need in this field, but until you spend your time doing experiments and real machine learning projects, you will never be able to master machine learning because you are only working with machine learning tools and algorithms, you can understand how its infrastructure works in reality.
In this article, we have tried to list the top machine learning projects for beginners, taking into account the main aspects of machine learning (supervised learning, unsupervised learning deep learning, and neural networks).
In all of these machine learning projects, you start with publicly available real-world datasets.
If you want to become a professional in machine learning, you should first gain experience with such projects. Now it’s time to put all the knowledge you’ve gathered through your books and tutorials to the test and build your machine-learning project!
Table of Contents
1. Stock price prediction
One of the best ideas to start experimenting with machine learning projects for beginners is to work on stock price prediction. Today, commercial organizations and companies are looking for software that can monitor and analyze the company’s performance and predict the future prices of various stocks.
With so much data available in the stock market, this center is a great opportunity for data scientists with an interest in finance.
However, before starting work, you should have a fairly good knowledge of the following areas:
Predictive Analytics: Using various artificial intelligence techniques for various data processes such as data mining, data exploration, etc. to predict the behavior of possible outcomes.
Regression analysis: Regression analysis is a type of forecasting technique based on the interaction between dependent (target) and independent (predictor) variables.
Action analysis: In this method, all the actions performed by the two methods mentioned above are analyzed, after which the result is fed into the machine learning memory.
Statistical Modeling: A process that involves constructing a mathematical description of a real-world process and explaining uncertainties, if any.
2. Prediction of sports matches by machine learning
- In the book Moneyball by Michael Lewis, the Oakland Athletics changed the face of baseball by incorporating the technique of scouting their players and analyzing it into their game plan. And just like them, you too can revolutionize sports in the real world!
Since there is no shortage of information and data in the world of sports, you can use this data to build fun and creative machine learning projects, such as using your own university’s sports statistics to predict which players will have the best career in Which sport (talented talent)?
You can also improve team management by analyzing the strengths and weaknesses of players on a team and classifying players accordingly. With all the statistics and sports information available, this is a great arena to accelerate your exploration and data visualization skills. And it can help you make your resume look more interesting than others. For anyone with some Python skills, Scikit-Learn will be an ideal choice as it includes a set of useful tools for regression analysis, classification, data entry, and more.
3. Creating a neural network by machine learning that can read handwriting
Deep learning and neural networks are the two main pillars that happen in artificial intelligence. These two bring us closer to technological wonders such as driverless cars, image recognition, etc. Therefore, now is the time for exploration in the field of neural networks.
Start your neural network machine learning project with the MNIST handwritten digit classification challenge. This user interface is very user-friendly and ideal for beginners.
4. Get a sentiment analyzer!
Although most of us use social media to communicate our personal feelings and opinions to the world, one of the biggest challenges is understanding the “feelings” behind social media posts…sounds like an interesting idea for your next machine learning project! Social media has a growing amount of user-generated content. Creating a machine learning system that can analyze the sentiments in texts or an article, makes it much easier for organizations to understand the behavior of their customers.
This, in turn, allows them to improve their customer service and thereby provide a basis for greater consumer satisfaction. To get started with your sentiment analysis project, you can mine data from Twitter or Reddit.
5. Movie ticket pricing system
With the spread of OTT platforms such as Netflix, and Amazon Prime, people prefer to watch their favorite movies easily. Factors such as pricing, content quality, and marketing have influenced the success of these operating systems.
The cost of making a professional film has increased a lot recently. And only 10% of the films that are made make a significant profit. The intense competition of TV and OTT platforms along with the high cost of tickets has made it harder to sell movie tickets.
An advanced ticket pricing system can help movie makers and viewers. With the increase in ticket demand and vice versa, the price of tickets can be higher or lower. The earlier a viewer buys a ticket, the less it costs for a movie in high demand. Your system should intelligently calculate pricing based on viewer interest, social signals, and supply-demand factors.
6. Strengthen the health system with machine learning!
Artificial intelligence and machine learning applications have started to infiltrate the healthcare industry recently and are rapidly changing the quality of global healthcare. Production of sanitary garments, remote monitoring, robotic surgery, etc., are all progressing with the help of machine learning algorithms using artificial intelligence.
Machine learning not only helps HCPs (healthcare providers) to provide faster and better healthcare but also significantly reduces the dependency and workload of doctors. So, why not use your skills to create a useful and impressive machine-learning project based on healthcare data?
The healthcare industry has a lot of data. Using this data, you can create diagnostic care systems that can automatically scan images, x-rays, etc., and provide an accurate diagnosis of possible future illnesses in each person, or programs for care. Create preventive measures that can predict and reduce the possibility of epidemics of diseases such as influenza, coronavirus, malaria, etc. in the community and at the national level.