Hey guys, I’m getting into machine learning/deep learning this summer and I was wondering what kinda “common” projects people make with ML (like a fullstack crud app for web dev). What have you guys made?

So I’ve been learning ML Theory for a while and I want to apply my learning to build cool projects. But things like CUDA or using cloud services are something I’m not sure how to do. I’m sure basic ml doesn’t need it but I’d like to get in the habit of using these tools.

Any suggestions would be appreciated or resources.

I'm a Junior in CS that's been trying to get an internship this summer, but I have yet to receive any offers despite having references from on campus networking events.

I'm just feeling really down about this, but I've decided to take on a side project to distract me while simultaneously improving my marketability.

I'd like to go in machine learning, so I've started looking into Tensorflow. What are some good side projects that utilize machine learning?

Frequently asked question, I know.

But I am looking for some project that I could use to ease myself into ML. I am currently learning the base math of it, but I want to create some actual projects at the same time too. I know Algebra, Linear Algebra, and at the moment I am beginning with Calculus.

I was thinking of some basic Image classificator, but thats something everyone does...what are some good (and maybe not too complex) projects to create as ML beginner? It can be image classification, but other ideas would be cool too. If someone could also provide a tutorial on YT about that project that would be even more helpfull.

What are some real-world ML project ideas. I am currently learning deep learning and want to build some resume worthy projects.

Hi, I’m looking for complete machine learning projects with code that utilize basic algorithms like regression, decision trees, and SVMs (but not LLMs). During my university studies, we covered machine learning topics in isolation—for example, one week on regression, another on hyperparameter optimization, then classification, deep learning, etc. However, we didn’t cover full projects that bring everything together or focus on deploying models.

Could you recommend any comprehensive examples, with code, that cover the entire process—data preprocessing, testing multiple models, hyperparameter tuning, and deployment?

Again. Code would be nice. ideally a published paper as well (optional) or it could be your private project.

Thanks!

I made a post last night about my journey to landing an AI internship and have received a lot of responses asking about side projects and learning resources, so I am making another thread here consolidating this information for all those that are curious!

Learning Process
Step 1) Learn the basic fundamentals of the Math

USE YOUTUBE!!! Literally just type in 'Machine Learning Math" and you will get tons of playlists covering nearly every topic. Personally I would focus on Linear Algebra and Calculus - specifically matrices/vector operations, dot products, eigenvectors/eigenvalues, derivatives and gradients.

It might take a few tries until you find someone that meshes well with your learning style, but
3Blue1Brown is my top recommendation.

I also read the book "Why Machines Learn" and found that extremely insightful.

Work on implementing the math both with pen and paper then in Python.

Step 2) Once you have a grip on the math fundamentals, I would pick up Hands-on Machine Learning with Sci-kit Learn, Keras and TensorFlow. This book was a game changer for me. It goes more in depth on the math and covers every topic from Linear Regression to the Transformers architecture. It also introduces you to Kaggle and some beginner level side projects.

Step 3) After that book I would begin on side projects and also checking out other similar books, specifically Hands on Large Language Models and Hands on Generative AI.

Step 4) If you have read all three of these books, and fully comprehend everything, then I would start looking up papers. I would just ask ChatGPT to feed you papers that are most relevant to your interests.

Beginner Side Project Ideas

1. Build a Neural Network from scratch, using just Numpy. It can be super basic - have one input layer with 2 nodes, 1 hidden layer with 2 nodes, and output layer with one node. Learn about the forward feed process and play around with different activation functions and loss functions. Learn how these activation functions and loss functions impact backpropagation (hint: the derivatives of the activation functions and loss functions are all different). Get really good at this and understand the difference between regression models and classification models and which activation/loss functions go with which type of model.

If you are really feeling crazy and are more focused on a SWE type of role, try doing it in a language other than python and try building a frontend for it so there is an interface where a user can input data and select their model architecture.

2) Build a CNN Image Classifier for the MNIST - Get familiar with the intricacies of CNN's, image manipulation, and basic computer vision concepts.

3) Build on top of open source LLM's. Go to Hugging Face's models page and start playing around with some.

4) KAGGLE COMPETITIONS - I will not explain further, do Kaggle Competitions.

Other Resources

I've mentioned YouTube, several books and Hugging Face. I also recommend:

DataLemur.com - Python practice, SQL practices, ML questions - his book Ace the Data Science Interview is also very good.

X.com - follow people that are prominent in the space. I joined an AI and Math Group that is constantly posting resources in there

deep-ml.com

If you have found any of this helpful - feel free to give me a follow on X and stay in touch @ x.com/hark0nnen_

I'm about to be a grad student in computer science with a ML specialization. I'm looking to build machine learning projects in my own time to best buff up my resume to apply for MLE internships/jobs

What kind of projects are recommended to do?

Hey everyone,
I have a few publications and patents and I work for a tier 2 company as Research scientist. Lately all my job applications have been rejected on the spot. Not even a first interview. I want to beef up my coding skills and be more attractive to employers. Maybe not having a huge github presence is hindering my prospects.

Can u please suggest opensource projects like SGLang or vLLm which I can contribute to? Any starting pointers?

Edit- treasure trove of comments below for any RS or MLE trying to get into faang. Thanks community.

AI/ML student seeking practical project ideas that solve real problems and stand out on a resume. Looking for suggestions that are feasible to build and aligned with what companies actually need today.

What are some basic ML and DL projects ideas according to what is trending now. I am currently at starting point and want to do a good project asap like in 20 days to 1 month.

I've recently done a project to predict house prices, but I wasn't able to go much further than that. What were some cool projects you followed that taught you skills, it'd be cool if you could link some youtube videos or some other resources as well

Hi everyone,

I’m a fourth-year engineering student, and I’ve recently become really fascinated by AI and machine learning. My goal is to not just understand the theory but also be able to build my own projects, from simple models to agents, and keep up with the exciting developments in this field.

I’ve got a solid foundation in math (linear algebra, statistics, multivariable calculus, etc.) and I’m comfortable with Python. I’ve also used tools like JupyterLab, MATLAB, and Google Colab for smaller projects. However, I feel like I’m still in the beginner zone when it comes to putting theory into practice.

I’d love to hear from you:

• Books: Are there any must-reads for someone getting started in AI/ML?

• Online courses/YouTube: What resources helped you the most when learning the basics?

• Project ideas: What beginner projects would you recommend to practice and build confidence?

• Keeping up-to-date: What blogs, newsletters, or websites do you follow to stay informed about AI developments?

• People to follow: Who inspires you in the AI/ML space?

I’m excited to dive deeper and really understand how everything works by building and experimenting. Any advice, resources, or project ideas would mean a lot!

Thanks in advance for your help! (ofc generated by chatpgt)

Hello everyone! I work as a machine learning researcher, and a few months ago, I've made the decision to step outside of my "comfort zone" and begin learning more about MLOps, a topic that has always piqued my interest and that I knew was one of my weaknesses. I therefore chose a few MLOps frameworks based on two posts (What's your MLOps stack and Reflections on working with 100s of ML Platform teams) from this community and decided to create an end-to-end MLOps project after completing a few courses and studying from other sources.

The purpose of this project's design, development, and structure is to classify an individual's level of obesity based on their physical characteristics and eating habits. The research and production environments are the two fundamental, separate environments in which the project is organized for that purpose. The production environment aims to create a production-ready, optimized, and structured solution to get around the limitations of the research environment, while the research environment aims to create a space designed by data scientists to test, train, evaluate, and draw new experiments for new Machine Learning model candidates (which isn't the focus of this project, as I am most familiar with it).

Here are the frameworks that I've used throughout the development of this project.

• API Framework: FastAPI, Pydantic

• Cloud Server: AWS EC2

• Containerization: Docker, Docker Compose

• Continuous Integration (CI) and Continuous Delivery (CD): GitHub Actions

• Data Version Control: AWS S3

• Experiment Tracking: MLflow, AWS RDS

• Exploratory Data Analysis (EDA): Matplotlib, Seaborn

• Feature and Artifact Store: AWS S3

• Feature Preprocessing: Pandas, Numpy

• Feature Selection: Optuna

• Hyperparameter Tuning: Optuna

• Logging: Loguru

• Model Registry: MLflow

• Monitoring: Evidently AI

• Programming Language: Python 3

• Project's Template: Cookiecutter

• Testing: PyTest

• Virtual Environment: Conda Environment, Pip

Here is the link of the project: https://github.com/rafaelgreca/e2e-mlops-project

I would love some honest, constructive feedback from you guys. I designed this project's architecture a couple of months ago, and now I realize that I could have done a few things different (such as using Kubernetes/Kubeflow). But even if it's not 100% finished, I'm really proud of myself, especially considering that I worked with a lot of frameworks that I've never worked with before.

Thanks for your attention, and have a great weekend!

I want to start working since I just finished a ML course at uni and also self taught myself some DL. What are some projects that will help me find a job since my prior job experiences were only manual labor

Im graduating in December from my undergrad, but I feel like all the projects I've done are pretty fairly boring and very cookie cutter. Because I don't go to a top school with great gpa, I want to make up for it by having something that the interviewer might think it's worthwhile to pick my brain on it.

The problem isn't that I can't find what to do, but I'm not sure how much of my projects should be "inspired" from the sample projects (like the ones here: https://github.com/firmai/financial-machine-learning).

For example, I want to make a project where I can scrape the financial data from ground up, ETL, and develop a stock price predictive model using LSTM. Im sure this could be useful in self learning, but it would it look identical to 500 other applicants who are basically doing something similar. Holding everything constant, if I were a hiring manager, I would hire the student who went to a nicer school.

So I guess my question is how can I outshine the competition? Is my only option to be realistic and work at less prestigious companies for a couple of years and work my way up, or is there something I can do right now?

If you are looking for ideas for AI Projects, ai-cases.com could be of help

I built it to help anyone easily understand and be able to apply important machine learning use-cases in their domain

It includes 40+ Ideas for AI Projects, provided for each: quick explanation, case studies, data sets, code samples, tutorials, technical articles, and more

Website is still in beta so any feedback to enhance it is highly appreciated!

Hello everyone

I’ve seen a lot of resume reviews on sub-reddits where people get told:

“Your projects are too basic”

“Nothing stands out”

“These don’t show real skills”

I really want to avoid that. Can anyone suggest some unique or standout ML project ideas that go beyond the usual prediction?

Also, where do you usually find inspiration for interesting ML projects — any sites, problems, or real-world use cases you follow?

Hey everyone,

I'm looking for some project suggestions, but I want to avoid the typical ones like credit card fraud detection or Titanic datasets. I feel like those are super common on every DS resume, and I want to stand out a bit more.

I am a B. Applied CS student (Stats Minor) and I'm especially interested in Data Engineering (DE), Data Science (DS), or Machine Learning (ML) projects, As I am targeting DS/DA roles for my co-op. Unfortunately, I haven’t found many interesting projects so far. They mention all the same projects, like customer churn, stock prediction etc.

I’d love to explore projects that showcase tools and technologies beyond the usual suspects I’ve already worked with (numpy, pandas, pytorch, SQL, python, tensorflow, Foleum, Seaborn, Sci-kit learn, matplotlib).

I’m particularly interested in working with tools like PySpark, Apache Cassandra, Snowflake, Databricks, and anything else along those lines.

Edited:

So after reading through many of your responses, I think you guys should know what I have already worked on so that you get an better idea.👇🏻

This are my 3 projects:

1. Predicting SpaceX’s Falcon 9 Stage Landings | Python, Pandas, Matplotlib, TensorFlow, Folium, Seaborn, Power BI

• Developed an ML model to evaluate the success rate of SpaceX’s Falcon 9 first-stage landings, assessing its viability for long-duration missions, including Crew-9’s ISS return in February 2025. • Extracted and processed data using RESTful API and BeautifulSoup, employing Pandas and Matplotlib for cleaning, normalization, and exploratory data analysis (EDA). • Achieved 88.92% accuracy with Decision Tree and utilized Folium and Seaborn for geospatial analysis; created visualizations with Plotly Dash and showcased results via Power BI.

2. Predictive Analytics for Breast Cancer Diagnosis | Python, SVM, PCA, Scikit-Learn, NumPy, Pandas • Developed a predictive analytics model aimed at improving early breast cancer detection, enabling timely diagnosis and potentially life-saving interventions. • Applied PCA for dimensionality reduction on a dataset with 48,842 instances and 14 features, improving computational efficiency by 30%; Achieved an accuracy of 92% and an AUC-ROC score of 0.96 using a SVM. • Final model performance: 0.944 training accuracy, 0.947 test accuracy, 95% precision, and 89% recall.

3. (In progress) Developed XGBoost model on ~50000 samples of diamonds hosted on snowflake. Used snowpark for feature engineering and machine learning and hypertuned parameters with an accuracy to 93.46%. Deployed the model as UDF.