Boost Your Resume with Machine Learning Portfolio Projects

Boost Your Resume with Machine Learning Portfolio Projects, Jobs for machine learning engineers are in high demand, but it might be difficult to be hired.

Employers are looking for experts with a range of machine learning difficulties under their belts.

There are just a few options for a novice or recent graduate to demonstrate abilities and experience.

They can either work on portfolio projects, open source projects, NGO projects as volunteers, or internships.

The machine learning portfolio projects that will strengthen your CV and assist you in the hiring process are the main topic of this article.

You become more adept at problem-solving when you work alone on a project.

What are the algorithms used in machine learning? »

Utilizing a deep learning model to degrade Mrna

A challenging regression issue is the mRNA degradation project. This project’s objective is to forecast degradation rates so that researchers can create vaccinations that are more stable in the future.

Although the project is two years old, you will learn a lot about using intricate 3D data manipulation and deep learning GRU models to solve regression problems.

We will also make predictions for five targets: reactivity, deg Mg pH10, deg Mg 50C, deg pH10, and deg 50C.

1. Automatic captioning of images

The project on your resume that is a must-have is automatic image captioning. You will gain knowledge of LSTM for natural language processing, CNN pre-trained models and computer vision.

In the conclusion, you will create the application on Gradio or Streamlit to present your findings. A brief description of the image will be produced by the image caption generator.

To predict captions in several languages, you can build your own deep learning architecture or locate numerous projects that are similar online.

The portfolio project’s main goal is to tackle a special issue. The model architecture may be the same, but the dataset may differ.

Your chances of landing a job will increase as you work with different data kinds.

Why Do So Many Data Scientists Quit Their Jobs? »

2. Deep Learning Stock Price Forecasting

The popular project idea of forecasting using deep learning will teach you a lot about the analysis of time series data, data management, pre-processing, and neural networks for time-series problems.

The forecasting of time series is not easy. Understanding seasonality, holiday seasons, patterns, and everyday volatility is important.

Simple linear regression may usually give you the best-performing model without the need for neural networks.

However, in the high-risk stock market, even a 1% difference represents millions of dollars in profit for the business.

3. Autonomous vehicle project

An advantage throughout the employment process comes from having a Reinforcement Learning project on your resume.

The hiring manager will presume that you have a knack for solving problems and that you are willing to learn more about challenging machine learning projects.

You will train the Proximal Policy Optimization (PPO) model for the Self-Driving automobile project in the OpenAI Gym setting (CarRacing-v0).

You should familiarise yourself with the basics of reinforcement learning before beginning the project because it differs significantly from other machine learning tasks.

You will test out multiple models and approaches throughout the project to enhance agent performance.

Python is superior to R for writing quality codes »

4. AI bot that can converse

AI for conversation is an enjoyable project. You will get knowledge of Facebook Blender Bot, Hugging Face Transformers, processing conversational data, and developing chatbot user interfaces (API or Web App).

Hugging Face offers a vast library of pre-trained models and datasets, so you can essentially fine-tune the model on a new dataset.

Your favorite movie character, a conversation between Rick and Morty, or a famous person you adore might all be considered.

In addition, you can modify the chatbot to suit your particular use case. Should a medical application arise.

The chatbot understands the patient’s emotions and requires technological knowledge.

5. Recognizing speech automatically

It’s hard to choose a favorite project. We now know everything there is to know about processing audio data, transformers, and enhancing model performance.

We needed two months to fully grasp the foundations before we could build the architecture that would support the Wave2Vec2 model.

Wav2Vec2 can be given a performance boost by adding n-grams and text preprocessing. In order to enhance the sound quality, we even pre-processed the audio data.

The Wav2Vec2 model can be adjusted for any language, which is the fun part.

6. End-to-end Machine Learning Project for NY Taxi Trips

Experience with end-to-end machine learning projects is a need. Your chances of being recruited are quite small without it.

You’ll discover:

data evaluation, data management, Building, and training models Monitoring experiments, pipelines for orchestration and machine learning, model execution, and Utilizing the cloud.

Surprising Things You Can Do With R »

Best practices for MLOps models monitoring

This project’s main goal isn’t to create the best model or discover a novel deep learning architecture.

The major objective is to become familiar with the methods and standards used in the industry for creating, implementing, and tracking machine learning systems.

You’ll discover a lot about how to build completely automated systems and about development operations.

Conclusion

We will strongly advise you to create a profile on GitHub or another code-sharing website after you’ve completed a few projects so you can publish your project findings and documentation there.

Working on a project is primarily done to increase your chances of landing a job. Skill is being able to promote yourself and the projects to a possible employer.

Create an entertaining web app with Gradio or Streamlit after finishing a project, start promoting it on social media, and make an interesting blog post.

Don’t forget the important quote “Keep Learning

Importance of Data Cleaning in Machine Learning »

You may also like...

Leave a Reply

Your email address will not be published.