Hands-On Learning: Practical Projects Offered in AI Training Courses

The more you try different things with the various AI projects, the more information you can gain. Artificial projects can help you in a way where they assist in showing your abilities to recruiters. Each project represents a different challenge and you can mention them while describing the projects. There is a difference between theoretical and practical knowledge where AI projects for students you can upgrade in your portfolio. During your training courses, you can try the projects and learn more about artificial intelligence- machine learning and its tools in creative ways. Here are some of the machine learning projects to acknowledge.

NINE Machine learning projects that you can try

The list of projects mentioned below apply to various industries which are from beginner to more advanced challenges. Each project needs to understand the theoretical aspects of machine learning algorithms to gain hands-on experience in solving real-world issues. Let’s delve into each project in detail.

1. Iris flower classification: Iris flower classification aims to categorize iris flowers into three which are setosa, versicolor and virginia based on the size of their petals and sepals. The objective of this machine learning is the basic classification of algorithms. The four features of sepal length, width, petal length and width.

2. House price prediction: This project centers around foreseeing the selling costs of houses based on various features including area, number of rooms, location etc., The idea behind this is the regression issues which help to understand the property features that affect their market value. The main objective of this project is to anticipate the cost of houses based on their features with the input features of size, location amenities etc.

3. Human activity recognition dataset: This includes distinguishing the actual activities of people from sensor information gathered from cell phones or wearable gadgets. It’s significant for applications like monitoring health and patient observation. The objective of this project is to identify the type of activity performed by a person and process time-series sensor information to perceive exercises.
4. Stock value forecast: The stock value prediction project is to forecast the future price according to historical data and potential market indicators. This is a challenging area because of the volatility and evolution of financial markets. The objective of stock value prediction is to foresee the stock prices and make investment decisions. The features required to develop these projects are historical stock prices, volumes and technical indicators.
5. Fraud detection: This system aims to provide fake activities which include different domains like credit card transactions, insurance claims or online services. This AI project is trained to detect the patterns indicating or signs of fraudulent activities. The objective of this project is to minimize the false positives and not to burden real clients or legit users. The features you need to develop are transaction details and understanding the behavior pattern.
6. Recommendation systems: This is an algorithm that provides suggestions on relevant items where users watch movies, books, and products according to their preferences and past behavior. You may have seen these widely used in e-commerce and entertainment platforms. The objective of this project is to improve the user experience with the recommendation options and increase sales and content interaction. The features required for this project are user-item interactions that include ratings, view and purchase options, and content features for genre, author or specifications.
7. Fake news detection: Now the information in online multiples and recognising the real and fake news which is crucial. This AI project recognises misleading or false information automatically. The objective of this project is to classify news articles, and stories which are based on the real and fake. Also categorisation is based on textual content for behavioral indicators. The features required are word usage, style and user engagement metrics like shares and comments.
8. Sales forecasting: This model foresees future sales according to historical data and other factors. This is crucial for business stock management, planning and strategic decision-making. The project objective is to predict the sales volume for the future and the signs affecting the sales trends. The features required for this project are historical data, seasonal effects, promotional activities and economic indicators.
9. Image recognition: This involves identifying and classifying objects within images where the fundamental tasks in computer vision are concerned with applications like security surveillance and autonomous vehicles. The objective of this project is to accurately identify the objects within images and build models which are across the various visual areas. The features of this project are pixel values and supervised learning to detect the picture marks.

You can feel certain when you know how to handle the advanced projects and improving your AI skills for the above-mentioned artificial intelligence projects will give you an idea. With the technical training in engineering colleges, graduates can develop and work on projects which are easy and significant. Practical knowledge will help you in the future in this field.