There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems.

For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects.

In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process. Working solo on the project also makes you better at problem-solving.

mRNA Degradation project is a complex regression problem. The challenge in this project is to predict degradation rates that can help scientists design more stable vaccines in the future.

The project is 2 years old, but you will learn a lot about solving regression problems using complex 3D data manipulation and deep learning GRU models. Furthermore, we will be predicting 5 targets: reactivity, deg_Mg_pH10, deg_Mg_50C, deg_pH10, deg_50C.

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 Streamlit 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.

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 dont 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.

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.

Conversational AI is a fun project. You will learn about Hugging Face Transformers, Facebook Blender Bot, handling conversational data, and creating chatbot interfaces (API or Web App).

Due to the huge library of datasets and pre-trained models available on Hugging Face, you can basically finetune the model on a new dataset. It can be Rick and Morty conversation, your favorite film character, or any celebrity that you love.

Apart from that you can improve the chatbot for your specific use case. In case of medical application. The chatbot needs technical knowledge and understands the patient's sentiment.

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.

End-to-end machine learning project experience is a must. Without it, your chance of getting hired is pretty slim.

You will learn:

The main purpose of this project is not about building the best model or learning new deep learning architecture. The main goal is to familiarize the industry standards and techniques for building, deploying, and monitoring machine learning applications. You will learn a lot about development operations and how you can create a fully automated system.

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. Dont 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.

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.

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7 Machine Learning Portfolio Projects to Boost the Resume - KDnuggets