Projects help you create a strong foundation of various machine learning algorithms and strengthen your resume. Best Machine Learning Projects For Resume & Final Year 2023.
Automatic Image Captioning
Automatic Image Captioning is the must-have project in your resume. You will learn about computer vision, CNN pre-trained models, and LSTM for natural language processing.
In the end, you will build the application on Streamlite or Gradio to showcase your results. The image caption generator will generate a simple text describing the image.
You can find multiple similar projects online and even create your deep learning architecture to predict captions in different languages.
The primary purpose of the portfolio project is to work on a unique problem. It can be the same model architecture but a different dataset. Working with various data types will improve your chance of getting hired.
Stock Price Forecasting using Deep Learning
Forecasting using Deep Learning is a popular project idea, and you will learn many things about time series data analysis, data handling, pre-processing, and neural networks for time-series problems.
The time series forecasting is not simple. You need to understand seasonality, holiday seasons, trends, and daily fluctuation. Most of the time, you don’t even require neural networks, and simple linear regression can provide you with the best-performing model. But in the stock market, where the risk is high, even a one percent difference means millions of dollars in profit for the company.
Self-Driving car project
Having a Reinforcement Learning project on your resume gives you an edge during the hiring process. The recruiter will assume that you are good at problem-solving and you are eager to expand your boundaries to learn about complex machine learning tasks.
In the Self-Driving car project, you will train the Proximal Policy Optimization (PPO) model in the OpenAI Gym environment (CarRacing-v0).
Before you start the project, you need to learn the fundamentals of Reinforcement Learning as it is quite different from other machine learning tasks. During the project, you will experiment with various types of models and methodologies to improve agent performance.
Automatic Speech Recognition
Automatic Speech Recognition is my favorite project ever. I have learned everything about transformers, handling audio data, and improving the model performance. It took me 2 months to understand the fundamentals and another two to create the architecture that will work on top of the Wave2Vec2 model.
You can improve the model performance by boosting Wav2Vec2 with n-grams and text pre-processing. I have even pre-processed the audio data to improve the sound quality.
The fun part is that you can fine-tune the Wav2Vec2 model on any type of language.
NY Taxi Trips: End-to-end Machine Learning Project
End-to-end machine learning project experience is a must. Without it, your chance of getting hired is pretty slim.
Conclusion
After working on a few projects, I will highly recommend you create a profile on GitHub or any code-sharing site where you can share your project findings and documentation.
The principal purpose of working on a project is to improve your odds of getting hired. Showcasing the projects and presenting yourself in front of a potential recruiter is a skill.
So, after working on a project, start promoting it on social media, create a fun web app using Gradio or Streamlit, and write an engaging blog. Don’t think about what people are going to say. Just keep working on a project and keep sharing. And I am sure in no time multiple recruiters will approach you for the job.
