7 Advanced AI Projects for Beginners

To break out of the beginner’s mindset and master essential skills, focus on building applications that solve real-world problems—avoid simple toy projects like flower classification or house price prediction. It’s time to move beyond basic projects and create something impactful, even with limited resources.

In this article, we will explore 7 beginner-friendly projects designed to help you build real-world AI solutions. Each project includes guides, example code, and visual resources to make them easy to understand and implement.

1. Stock Market Forecasting with TimeGPT

 
Link to the project: Time Series Forecasting With TimeGPT

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In this project, you will explore TimeGPT, a cutting-edge time series forecasting model accessible via the Nixtla API. Instead of training a machine learning model from scratch, you will fine-tune TimeGPT to generate accurate forecasts in seconds, saving hours of computation. You will also learn how to handle complex, multivariate datasets and dive into the Nixtla ecosystem for advanced forecasting tools.

This project is perfect for quickly mastering state-of-the-art time series forecasting techniques.

2. Multilingual Automatic Speech Recognition

 
Link to the project: kingabzpro/Urdu-ASR-SOTA

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This unique project focuses on creating a state-of-the-art speech recognition system for low-resource languages like Urdu. With a simple guide, Jupyter notebook, source code, and additional resources, you will learn to build and fine-tune an Automatic Speech Recognition (ASR) model, overcoming challenges faced by lesser-resourced languages.

After fine-tuning your model, you will develop an AI app and deploy it on Hugging Face Spaces, making it accessible to everyone. This hands-on project enhances your skills, significantly boosting your chances of getting hired in the AI field.

3. Image Segmentation Using Text and Image Prompts

 
Link to the project: timojl/clipseg

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In this project, you will explore CLIPSeg, a cutting-edge model for image segmentation using both image and text prompts. With the provided code, Jupyter notebooks, and resources, you will learn how to leverage CLIP’s multimodal capabilities to perform zero-shot or fine-tuned image segmentation tasks. By the end of the project, you will have hands-on experience in building and customizing segmentation models, gaining valuable skills in computer vision and multimodal AI.

4. Anomaly Detection

 
Link to the project: MVTec-AD : Anomaly Detection with Anomalib Library

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This project focuses on developing models for anomaly detection in images, a critical task in identifying unusual patterns that deviate from expected behavior. Using the MVTec AD dataset, you will learn to detect defects in manufacturing processes or identify unusual activities in surveillance footage.

By the end of this project, you will have hands-on experience in building and fine-tuning models for quality control and security applications, gaining valuable skills in computer vision and machine learning.

5. AI Plays Super Mario Bros

 
Link to the project: Building a Deep Q-Network to Play Super Mario Bros

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In this project, you will build and train a Double Deep Q-Network (DDQN) to play the classic game Super Mario Bros. Using reinforcement learning techniques, you will teach an AI agent to navigate the game environment, overcome obstacles, and achieve high scores. The project leverages OpenAI Gym (gym-super-mario-bros library) and a Double DQN architecture, which improves upon traditional Deep Q-Networks by reducing overestimation bias during training.

This project is perfect for anyone looking to explore the exciting intersection of AI and gaming while building expertise in advanced machine learning techniques.

6. Fine-tuning Llama 3.2 and Using It Locally

 
Link to the project: Fine-tuning Llama 3.2 and Using It Locally: A Step-by-Step Guide

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Fine-tuning large language models (LLMs) has become essential for unlocking their full potential in narrow AI applications. It allows you to customize models for tasks like text classification, language translation, style transfer, code generation, and building specialized AI applications.

In this project, you will fine-tune the open-source LLaMA 3.2 model on an e-commerce dataset to create a fully functional customer assistant chatbot.

After fine-tuning, you will learn to quantize the model and convert it into the llama.cpp format, enabling efficient deployment on local systems.

7. How to Deploy LLM Applications Using Docker

 
Link to the project: How to Deploy LLM Applications Using Docker: A Step-by-Step Guide

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This project introduces you to the operational side of AI applications, focusing on building and deploying a simple AI application using Docker on Hugging Face Spaces. While deploying AI models might sound complex, this step-by-step guide simplifies the process, making it accessible even for non-technical users. You will learn how to containerize your application, ensuring it runs consistently across different environments, and deploy it to the cloud with minimal effort.

By the end of this project, you will have hands-on experience in deploying Large Language Model (LLM) applications, gaining valuable skills in dockerization and cloud deployment.

Final Thoughts

 
Building advanced AI projects is the best way to master essential skills, explore new and efficient tools, and gain hands-on experience with diverse AI methodologies and models. These projects not only enhance your technical expertise but also provide the practical knowledge needed to tackle real-world challenges. By working on such projects, you will develop a strong portfolio that showcases your abilities, making you stand out in the competitive AI field.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.