Image by Author
Learning by doing is the best way to master essential skills for becoming a machine learning engineer. Instead of just focusing on simple classification and regression models.
In this blog, we will focus on advanced machine learning projects that will impact your resume and attract recruiters and hiring managers. We will learn about computer vision projects, speech recognition, stock price forecasting, fine-tuning Stable Diffusion and Llama 3, multi-step AI agent applications, and reinforcement learning. You will also learn about diverse tools and concepts to build and optimize these projects.
1. Automatic Image Captioning
Automatic Image Captioning is a fascinating project that combines computer vision and natural language processing. The goal is to generate descriptive captions for images. This project uses convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) to generate captions. Implementing this project will demonstrate your ability to work with complex neural network architectures and handle multimodal data.
Image from the project
2. Automatic Speech Recognition
Automatic Speech Recognition (ASR) systems convert spoken language into text. This project can be particularly impressive if you work with a less common language. It is by far the most popular project I have ever worked on. You can even see for yourself by going to the link kingabzpro/wav2vec2-large-xls-r-300m-Urdu and checking the number of downloads.
In this project, you will learn to process both text and audio and then fine-tune the wav2vec2 model in the language of your choice. If you are looking for the code source and guide, you could check out the kingabzpro/Urdu-ASR-SOTA DagsHub repository.
After fine-tuning the model, you can save it on Hugging Face and then build a real-time ASR app to deploy on the Hugging Face space, as shown below.
3. Stock Price Forecasting
Stock Price Forecasting involves predicting the future prices of stocks using historical data. This project can be implemented using various machine learning techniques such as time series analysis, regression models, and even deep learning models like LSTMs (Long Short-Term Memory networks). You can even use what you learned from this project to build your own trading bot by integrating the stock exchange API.
Image from the project
4. Fine-tuning Stable Diffusion XL
Stable Diffusion XL is a powerful model for generating high-quality images. Fine-tuning this model using techniques like DreamBooth and LoRA (Low-Rank Adaptation) can help you create customized image generation models. In this project, I have fine-tuned the model using 5 of my images, and the results are amazing.
You can fine-tune it on specific cartoon characters and design your own comic book using Generative AI. This project will showcase your expertise in working with state-of-the-art generative models and your ability to customize and optimize them for specific tasks.
5. Fine-Tuning Llama 3 and Using It Locally
The tutorial “Fine-Tuning Llama 3 and Using It Locally” covers the project of fine-tuning the latest top-of-the-line open-source model, Llama 3, on a medical dataset. The goal is to build a chatbot where users can ask questions to an AI doctor.
Throughout the tutorial, you will learn how to process the data, use LoRA techniques, optimize the model and memory, accelerate the model using GPUs, and use various tools for merging, converting, and quantizing the model.
At the end, you will download the fine-tuned quantized model and use it locally using the Jan application. This project is not only fun, but also a great learning opportunity through which you will gain a deep understanding of how to troubleshoot various issues related to fine-tuning large language models.
Image from the project
6. Build a Multiple-step AI Agent using LangChain
Building a multi-step AI agent involves creating a system that can perform a series of tasks autonomously. Using frameworks like LangChain, you can develop AI agents that can handle complex workflows.
In this project, you will create an AI application that takes a user’s query to search the web using the Tavily API and also generates Python code to use the data. The application will then use Python REPL to execute the code and return the visualization requested by the user. Before starting the project, you will learn about the Cohere API and its various features.
Screenshot from the project
7. Building MLAgent for 2v2 Soccer Game
Reinforcement learning is a powerful technique for training agents to make decisions in complex environments. Building an MLAgent for a 2v2 soccer game involves creating an environment, defining rewards, and training agents using reinforcement learning algorithms. Hugging Face offers hands-on tutorials for such projects as part of the DeepRL course that you can take for free. This project will showcase your expertise in reinforcement learning and game development and your ability to create intelligent agents that can learn and adapt.
Image from the project
Conclusion
Working on these advanced machine learning projects will enhance your technical skills and make your resume stand out to recruiters and hiring managers. Each project covers different aspects of machine learning, from computer vision and natural language processing to reinforcement learning and generative models. By showcasing your ability to handle complex projects and diverse datasets, you will significantly increase your chances of landing a high-paying machine learning job.
About Abid Ali Awan
Abid Ali Awan is the Assistant Editor of KDnuggets. Abid 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.
