30 Machine Learning Projects for Your Portfolio (2026)

Build a classifier to predict equipment failure from sensor data, focusing on feature engineering and model evaluation.

Implement a CNN on MNIST dataset, tuning hyperparameters and visualizing model predictions.

Use TF-IDF and logistic regression to classify review sentiment, including basic text preprocessing.

Predict housing prices using linear regression and decision trees, with emphasis on data cleaning and RMSE metrics.

A classic project extended with cross-validation and confusion matrix analysis to solidify classification concepts.

Predict which customers will leave using a dataset, applying imbalanced data techniques like SMOTE.

Forecast stock prices or sales data using ARIMA models, focusing on stationarity and autocorrelation.

Use a pre-trained MobileNet model to classify images from CIFAR-10, learning transfer learning basics.

Build a Naive Bayes classifier to detect spam emails, incorporating text vectorization techniques.

Implement anomaly detection using isolation forests or logistic regression on an imbalanced dataset.

Implement YOLOv8 using PyTorch for real-time object detection on video streams, optimizing with CUDA.

Fine-tune a BART model from Hugging Face for abstractive text summarization on news articles.

Build an end-to-end ML pipeline for a Kaggle competition, tracking experiments and models with MLflow.

Implement neural style transfer using PyTorch, combining content and style losses for artistic images.

Deploy a fine-tuned Hugging Face transformer model using FastAPI and Docker, including load testing.

Solve the CartPole environment using Deep Q-Networks (DQN) with PyTorch, focusing on reward shaping.

Implement U-Net architecture for medical image segmentation, using TensorFlow and Dice coefficient metric.

Fine-tune BERT for multi-class classification on a custom dataset, using Hugging Face transformers.

Build an LSTM autoencoder in TensorFlow to detect anomalies in server metrics or financial data.

Optimize a neural network's hyperparameters using Optuna, comparing Bayesian optimization to grid search.

Code a Vision Transformer (ViT) from scratch in PyTorch, training on ImageNet subset with mixed precision.

Use RLlib or PyTorch to train multi-agent systems in StarCraft II environment, implementing MADDPG or QMIX.

Build a scalable MLOps platform using MLflow, Kubeflow, and Kubernetes for model training, serving, and monitoring.

Fine-tune a CodeGen or StarCoder model on a custom code dataset for specific programming tasks.

Implement federated learning with TensorFlow Federated, simulating multiple clients training a model collaboratively.

Implement a PointNet++ model for 3D object detection using LiDAR point cloud data from KITTI dataset.

Build a system that classifies emotions from speech in real-time using Mel-spectrograms and CNNs/Transformers.

Create a basic AutoML system that automates feature engineering, model selection, and hyperparameter tuning.

Implement StyleGAN2 or Progressive GANs to generate high-resolution faces or artwork, focusing on training stability.

Build a Neural Architecture Search system using reinforcement learning or evolutionary algorithms to find optimal CNN architectures.