RESOURCES
Scratch
Beginner
Division Recommendation:
Scratch is a visual, block-based programming platform that introduces beginners to programming intuitively and interactively. Using specialized extensions in Scratch AI, Machine Learning for Kids, or Google’s Teachable Machine, students can train simple models using labelled data, experiment with image and text classification, and see how AI recognizes patterns.
How it Can Be Used in AI Projects: Scratch provides basic AI capabilities through extensions such as text-to-speech and machine learning models. Students can use Scratch to build simple AI-powered chatbots, interactive educational games, or even emotion recognition projects with image inputs. Check out a vast selection of projects from Machine Learning for Kids that use Scratch.
MIT App Inventor
Beginner, Junior
Division Recommendation:
MIT App Inventor is an open-source, web-based platform that enables students to design and develop Android applications using a block-based interface. Simplifying app creation allows beginners to build a functional mobile application without writing extensive code.
How it Can Be Used in AI Projects: App Inventor offers built-in AI capabilities, such as speech recognition, image classification and speech-to-text functions, which students can integrate into the mobile apps. Students can create AI-powered voice assistants, translation apps or accessibility tools for visually impaired users. Explore the tutorials created by MIT that integrate AI into various applications.
Thunkable
Beginner, Junior
Division Recommendation:
Thunkable is a no-code mobile app development platform that lets students build cross-platform applications for both iOS and Android devices using drag-and-drop interfaces. It also offers additional advanced features such as custom user interface components, third-party integrations, and cloud-based storage.
How it Can Be Used in AI Projects: Thunkable lets students integrate AI APIs such as Google Vision, OpenAI’s GPT models, and voice recognition into mobile applications. Students can experiment with AI-driven features and develop projects like AI-powered chatbots, mood-based music recommendation apps, or smart home control systems. Read through this to learn about the ways to connect AI to Thunkable.
Web Apps
Junior, Senior
Division Recommendation:
Web applications, or web apps, are interactive software programs that run in a web browser instead of being installed on a device. They are developed using a combination of front-end programming languages such as HMTL, CSS, and JavaScript and back-end frameworks such as Flask, Django, and Node.js.
How it Can Be Used in AI Projects: Web apps allow AI models to be accessible from anywhere with an internet connection, so students can use AI to implement features like sentiment analysis, automated content generation, or real-time object recognition. Students can develop web-based AI applications such as recommendation systems, AI-powered search engines, or interactive AI tutors. Read to learn how AI can be integrated into web applications.
Python
Junior, Senior
Division Recommendation:
Python is the main programming language used in machine learning models. It is a high-level, general-purpose programming language known for its intuitive syntax, readability, powerful framework, and extensive libraries supporting the development of AI and machine learning.
How it Can Be Used in AI Projects: Python serves as the foundation for many AI projects, enabling students to build machine learning models, automate data processing, and develop AI-driven applications. It is commonly used in natural language processing (NLP), predictive analytics, and robotics. Check out this Python tutorial, which uses Google Colab, an interactive computing platform, where you can execute Python code directly in a browser.
Scikit-Learn
Junior, Senior
Division Recommendation:
Scikit-Learn is a Python library that provides simple and efficient tools for data mining, data analysis, and machine learning development, including classification, regression, clustering, and dimensionality reduction, alongside utilities for model evaluation and preprocessing. Its versatility and efficiency make it a go-to library for machine learning.
How it Can Be Used in AI Projects: Students can train and evaluate AI models for tasks such as handwriting recognition, spam detection, and predictive analytics. Scikit-Learn simplifies the process of implementing supervised and unsupervised learning models, allowing students to explore concepts like decision trees, support vector machines, and k-nearest neighbours. Browse the gallery of examples demonstrating the usage and applications of Scikit-Learn.
Pandas
Junior, Senior
Division Recommendation:
Pandas is a Python library used for data manipulation and analysis, which provides high-performance, easy-to-use data structures, such as Series and DataFrame, simplifying the work with structured data. It provides powerful tools for data cleaning, transformation, and aggregation, making it essential for exploratory data analysis (EDA).
How it Can Be Used in AI Projects: Pandas is essential for cleaning and preprocessing datasets before feeding them into AI models. Students can use it to analyze survey data, process images for machine learning, or prepare text for NLP applications. Learn how to use pandas in a quick 10-minute tutorial.
PyTorch
Senior
Division Recommendation:
PyTorch is an open-source deep learning framework and is also widely used as a machine learning framework, all based in Python. It provides dynamic computation graphs and functionalities like building a machine learning model from pre-configured blocks, loading data from files of various formats, and executing model training with just a few lines, making it highly flexible for deep learning research and development.
How it Can Be Used in AI Projects: PyTorch allows students to develop deep learning models and neural networks for applications like natural language processing, image recognition and classification, language translation, and generative AI. Check out these tutorials to explore PyTorch.
NumPy
Senior
Division Recommendation:
NumPy is a fundamental library for numerical computation in Python, while supporting large multi-dimensional arrays and matrices, and a collection of mathematical functions and operations for them. It enables efficient operations on large datasets using vectorization, making it indispensable for high-performance computing and mathematical modeling.
How it Can Be Used in AI Projects: NumPy is essential for handling numerical data, optimizing AI model computations, implementing deep learning algorithms, such as neural networks from scratch, performing matrix transformations, and processing large datasets. Read this to understand the basics of NumPy.
Matplotlib
Senior
Division Recommendation:
Matplotlib is a flexible Python plotting library for creating static, animated, and interactive data visualization. Students can create a variety of highly customizable plots, including line graphs, bar charts, scatter plots, and histograms. It is widely used for data exploration and presentation, making it a key tool for data visualization.
How it Can Be Used in AI Projects: Students can use Matplotlib to analyze and visualize datasets, trends, relationships and patterns before building predictive models, model performance, and make AI predictions through the graphs and plots. Look at this gallery, which contains example plots using Matplotlib.
Seaborn
Senior
Division Recommendation:
Seaborn is a Python data visualization library built on Matplotlib. It was designed to create an intuitive and visually appealing way to display statistical relationships in data and graphics, while providing high-level functions for generating complex visualizations such as heatmaps, violin plots, and pair plots, making it widely used in data science and exploratory data analysis (EDA).
How it Can Be Used in AI Projects: Students can present insights through visually appealing and highly detailed statistical visualizations, making it easier for students to interpret AI model performance. This makes it useful for analyzing bias in AI models, visualizing time-series data, and creating in-depth comparisons of machine-learning results. Check out the gallery of examples of how you can apply Seaborn in projects.
Google Colab
Senior
Division Recommendation:
Google Colab is a cloud-based Jupyter Notebook environment that allows students to write and execute Python code with access to powerful computing resources like GPUs. It supports both simple code and complex code seen when building and training machine learning models.
How it Can Be Used in AI Projects: Google Colab enables students to train AI models efficiently, work with large datasets, and collaborate on projects without requiring high-end local hardware.
TensorFlow
Additional
Division Recommendation:
TensorFlow is an open-source machine learning framework that lets students build, train, and deploy scalable AI models efficiently for deep learning model creation while boasting a vast pool of libraries, tools, and community resources accessible to all students. With support for both high-level APIs and low-level operations, it can create and optimize models for applications such as image recognition, natural language processing, and reinforcement learning.
How it Can Be Used in AI Projects: TensorFlow is commonly used in deep learning applications such as image classification, speech recognition, recommendation systems, and predictive analytics. Students can also use it to build convolutional neural networks (CNNs) for tasks like object detection and facial recognition; recurrent neural networks (RNNs) for text generation and speech synthesis; and transformer models for natural language processing (NLP) applications like chatbots and language translations. Explore the TensorFlow tutorials; at the top of each tutorial, you'll see a Run in Google Colab button - click the button to open the notebook and run the code.
OpenCV
Additional
Division Recommendation:
Open Source Computer Vision (OpenCV) is a powerful open-source computer vision and machine learning library used for real-time image and video processing. It provides a vast collection of tools and algorithms for tasks such as object detection, face recognition, edge detection, and video analysis. This is a go-to choice for many researchers and developers working on computer vision projects.
How it Can Be Used in AI Projects: Students can use OpenCV for face detection, gesture recognition, object tracking, and augmented reality applications. Check out the OpenCV tutorials.
NLTK
Additional
Division Recommendation:
The Natural Language Toolkit (NLTK) is a comprehensive library in Python designed to process and analyze human language data and provide tools for tasks such as tokenization, parsing, and text classification. NLTK supports applications in natural language processing (NLP) such as sentiment analysis, machine translation, and information retrieval.
How it Can Be Used in AI Projects: NLTK allows students to build AI models for text-based applications including chatbots, text classifiers, sentiment analysis models, and AI-powered translation tools. Explore the list of examples of NLTK usage.
Streamlit
Additional
Division Recommendation:
Streamlit is a Python framework that simplifies creating interactive and data-driven web applications specifically for machine learning and data science projects. With its user-friendly approach, students do not need extensive web development knowledge, making it great for rapid prototyping and sharing data applications.
How it Can Be Used in AI Projects: Students can use Streamlit to develop AI-powered applications, dashboards, and data analysis tools while creating user-friendly AI applications without needing extensive web development experience. Discover the gallery of apps integrating Streamlit.