Artificial intelligence (AI) has long been regarded as one of the most advanced areas in the computer world. The use of AI applications is continuously expanding, and tech aficionados must stay up with this fast-changing sector in order to work with AI-driven tools and apps. The majority of organizations that integrate AI into their workforce follow a similar implementation methodology. They devise a flawless proof of concept and team up with an AI vendor who pledges to launch the system on their behalf. And having a practical understanding of whatever technology you’re working on is required to excel at building industry-oriented AI solutions. Although textbooks and other study materials will offer you all of the textual information you want about any technology, working on open-source AI projects can help you master AI concepts.
In this post, we’ll go through the top 10 AI project ideas for beginners that are appropriate for novices and people who are just getting started with machine learning. In addition, this list can come in handy for data scientists who are looking to diversify their professional portfolio and expertise in various industry-related applications of AI and machine learning.
Predicting Wine Quality
It is true that the older the wine, the better it will taste. However, age isn’t the only factor that influences a wine’s flavor. You will use fixed acidity, volatile acidity, alcohol, and density to assess the quality of wine in this project.
In this AI project, you’ll create an ML model that can look at a wine’s chemical features and estimate its quality. There are roughly 4898 observations in the wine quality dataset you’ll be utilizing for this project, with 11 independent variables and one dependent variable. Fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulfates, and alcohol are some of the input variables. Quality is the outcome variable which is determined by sensory data with scores between 0 and 10.
In this project, you will get exposure to data visualization, data exploration, regression models, and more.
Dataset: Wine Quality Dataset
Enron Email Project
The Enron crisis and its subsequent collapse were some of the most significant business failures in history. Enron was one of America’s major energy companies in the year 2000. After being exposed for fraud, it went bankrupt in less than a year.
On the positive side, the dataset of emails from Enron was retained. The Enron email dataset consists of 500 thousand emails sent between 150 former Enron workers, the majority of whom were top executives. It’s also the only significant publicly accessible collection of genuine emails, making it more useful for natural language processing. This project on AI entails creating a machine learning model that detects fraudulent behavior using the k-means clustering technique. According to comparable patterns in the dataset, the model will divide the observations into ‘k’ number of clusters.
Dataset: Enron Investigation Dataset
Boston House Pricing using Machine Learning & Python
This is one of the best AI projects for students to learn about forecasting the price of a property based on data from nearby homes. In this project, interested people can learn how to predict prices on the basis of new data.
The Boston housing dataset contains information on various houses in Boston based on criteria such as tax rates, crime rates, and the number of rooms in each property. It’s an exceptional dataset for estimating the values of various Boston homes. In this project, you can employ linear regression to create a model that can forecast the price of a new home. Since this data shows a linear connection between the input and output values and when the input is unknown, employing linear regression is the ideal choice for this project. You can also employ more nuanced methods like random forest regressor or gradient boosting to predict house prices.
Dataset: Boston housing dataset
Working on the Iris Flowers categorization AI project idea is one of the finest ways to experiment with machine learning concepts like classification using the iris flowers dataset. Because iris blooms come in a variety of species, the length of the sepals and petals may be used to differentiate them. This machine learning project aims to sort the flowers into one of three species: Virginica, Setosa, or Versicolor.
The iris flowers dataset includes quantitative parameters such as the length and breadth of sepals and petals. It’s ideal for learning about supervised machine learning techniques, specifically how to load and handle data, while correctly categorizing irises into one of three species.
Dataset: Iris Flowers dataset
Creating your own emoji
Emojis and avatars have been ingrained in internet conversation, product reviews, brand sentiment, and a variety of other activities. It also resulted in an increase in data science research into emoji-driven storytelling.
Thanks to advances in computer vision and deep learning, it is now feasible to discern human emotions from photos. In this project, you will classify human face emotions using deep learning algorithms to filter and map matching emojis or avatars — similar to how Snapchat creates Bitmoji.
The FER2013 dataset comprises grayscale face images with a resolution of 48*48 pixels. The photos are equally spaced and centered. This dataset includes the following facial emotions viz., angry, disgust, fear, happy, sad, surprise, and natural.
The goal of this AI project is to create a convolutional neural network architecture and train it using the FER2013 dataset to recognize emotions from photos. After identifying the facial expressions in the images, you will map the emotion to an emoji or an avatar.
MNIST Handwritten Digit Classification
The MNIST digit classification AI project in Python aims to teach computers how to detect handwritten numbers. Since working with image data is more difficult than flat relational data, the MNIST dataset is ideal for someone who is just getting started in deep learning. You will utilize the MNIST datasets to train your ML model using Convolutional Neural Networks (CNNs) in this project. Despite the fact that the MNIST dataset may fit in your PC RAM (it is relatively tiny), handwritten digit identification remains a complex process. The MNIST dataset is a modified subset of two datasets gathered by the National Institute of Standards and Technology in the United States. It has 70,000 handwritten digits that have been labeled.
The MNIST dataset was created using Python’s Keras package. Therefore, you can get started with this AI project by installing Keras, importing the library, and loading the dataset.
Recommendation Engines for Next Binge
Today, online streaming platforms are a huge hit among the millennials and gen-z. These streaming platforms also offer recommendations on what to watch next, based on a viewer’s past viewing habits and interests. This is accomplished by machine learning, and it may be a fun and simple project for people who have a working knowledge of machine learning algorithms. Working on this AI open source project idea can allow you to develop a recommendation engine (similar to those used by Amazon and Netflix) that can provide tailored suggestions for items, movies, music, and so on based on consumer preferences, requirements, and online activity.
The MovieLens 25M movie rating dataset comprises 25 million ratings and one million tag applications applied to 62,000 movies by 162,000 users, making it one of the most diversified dataset selections. It also includes tag genome data with 15 million relevance scores across 1,129 tags.
Dataset: Movielens Dataset
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Uber Data Analysis Project
This intriguing AI project idea for beginners may help them understand how to visualize data on the Uber platform. This dataset can help you figure out how to evaluate the rides so that you can make business changes. The ride-sharing app needs to have a superior support system to fix consumer complaints as rapidly as possible, with billions of rides to manage each year.
As a result, Uber created Customer Obsession Ticket Assistant, or COTA, a “human-in-the-loop” model architecture to increase the performance of its customer support staff.
The Uber team employed deep learning to identify the influence on ticket processing time, customer happiness, and income by split-testing two versions of COTA, viz., Pre-processing transformations using Spark and Deep learning training using TensorFlow. It’s an outstanding model for deep learning projects that combines brilliant technological design with human involvement, and it should inspire you to create your own deep learning initiatives.
Dataset: Uber Data Analysis Dataset
Prediction of Breast Cancer
Artificial intelligence and machine learning technologies have already begun to permeate the healthcare business and are fast changing the face of global healthcare. Be it for early identification of Parkinson’s Disease or cancerous cells, AI has helped revolutionize the healthcare industry with its innovative solutions.
One of the commonly known healthcare datasets for AI open source project ideas is Breast Cancer Wisconsin Diagnostic Dataset. The difficulty to discern between benign (non-cancerous) and malignant (cancerous) tumors is a major issue in breast cancer detection. You’ll need to classify whether a tumor is malignant or not based on metrics like “radius mean” and “area mean” of the tumor in the dataset. While this dataset is already present in a pre-processed form, it requires extensive analysis to find optimum results at higher accuracy. Finding a minimal error rate is crucial as any miscalculation can prove lethal to patients’ lives. Make sure to have a working knowledge of random forest and XGBoost, as they are some of the most important concepts implemented in this AI project.
Because healthcare organizations have access to large patient data, you may get insight into designing diagnostic care systems that can automatically scan pictures, X-rays, and other images and deliver an accurate diagnosis of likely ailments by analyzing this data.
Audio to Text Translation
Voice AI is one of the trending concepts in the AI industry. Taking advantage of the demand for sophisticated voice AI algorithms that power voice assistants like Alexa to AI chatbots, you can design a project that employs AI open-source datasets using NLP. The librispeech dataset is an enormous collection of English speech data derived from audiobooks from the LibriVox project. It is the ideal dataset for voice recognition because it contains over 1000 hours of English-read talks in diverse accents. The file format of data is in the form of FLAC (Free Lossless Audio Codec) without any loss in quality or loss of any original audio data. This dataset is used in various applications, including automated speaker verification and speaker identification. The objective of this project is to develop a model that can convert audio into text automatically. You’ll create a voice recognition system that can recognize English speech and convert it to text.
Dataset: Librispeech Dataset
Here is a comprehensive list of AI open-source project ideas. AI is still at an early stage in the tech industry domain. There are a lot of initiatives that are currently being worked upon to address some real-world challenges while simultaneously improving the existing models. This list of AI open-source project ideas covers everything from the fundamentals like linear regression to advanced techniques like transformer and LSTM. It was curated on the idea that helps both students and professionals get insight into the industry applications of AI and machine learning concepts.