40 Machine Learning Projects to Land Your First Job (2026)

Build a linear regression model to predict housing prices using a dataset like California Housing, focusing on data preprocessing and evaluation.

Create a neural network to classify MNIST digits, implementing a basic CNN and visualizing predictions.

Develop a text classifier to distinguish spam from ham emails using Scikit-learn's Naive Bayes and TF-IDF.

Implement a multi-class classifier for the Iris dataset using decision trees and random forests, with hyperparameter tuning.

Predict customer churn for a telecom dataset using logistic regression and evaluate with precision-recall curves.

Use a pre-trained transformer model from Hugging Face to analyze sentiment in movie reviews, focusing on pipeline usage.

Forecast stock prices or weather data using ARIMA models in Python, emphasizing time series decomposition.

Build a data augmentation pipeline for image datasets using TensorFlow's ImageDataGenerator to improve model robustness.

Create a simple movie recommendation system using collaborative filtering with the MovieLens dataset.

Deploy a trained model as a REST API using Flask, including basic input validation and response formatting.

Implement a YOLO-based object detector on custom datasets, training from scratch or fine-tuning pre-trained weights.

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

Apply neural style transfer using PyTorch to blend artistic styles with photographs, optimizing for visual quality.

Build an LSTM-based model to detect anomalies in sensor data, focusing on sequence modeling and threshold tuning.

Create a model that assigns multiple labels to images from datasets like COCO, using custom loss functions.

Develop an NER system combining SpaCy's pipelines with transformer embeddings for high accuracy on custom text.

Implement a DQN agent to solve OpenAI Gym's CartPole environment, including experience replay and target networks.

Build an end-to-end ML pipeline for a Kaggle competition, using MLflow to log experiments, parameters, and metrics.

Train a U-Net model for semantic segmentation on medical images or satellite data, emphasizing IoU metrics.

Fine-tune a BERT model on SQuAD dataset for extractive question answering, optimizing for F1 score.

Automate hyperparameter tuning for a neural network using Optuna, comparing Bayesian optimization with grid search.

Containerize and deploy a Hugging Face transformer model using FastAPI and Docker, ensuring scalability and monitoring.

Code a Vision Transformer (ViT) from scratch in PyTorch, training on ImageNet subsets and comparing to CNNs.

Develop a deep RL agent using Proximal Policy Optimization (PPO) in a simulated driving environment like CARLA.

Fine-tune CLIP for zero-shot image-text matching on custom datasets, exploring cross-modal retrieval.

Design a production-grade MLOps pipeline using Kubeflow for model training, deployment, and monitoring on cloud infrastructure.

Build a GAN (e.g., StyleGAN) to generate high-resolution faces or artwork, focusing on training stability and quality metrics.

Fine-tune a large language model like Llama 2 using Low-Rank Adaptation (LoRA) for a specific task like code generation.

Implement DeepSORT for real-time multi-object tracking in video streams, integrating with YOLO for detection.

Simulate federated learning across multiple clients using PyTorch, addressing challenges like non-IID data and communication efficiency.

Fine-tune OpenAI's Whisper model for low-resource language transcription, optimizing for accuracy and latency.

Implement a neural architecture search algorithm using reinforcement learning or evolutionary strategies to design optimal networks.

Train a PointNet model for classifying 3D point cloud data from datasets like ModelNet40, handling spatial transformations.

Apply quantization and pruning techniques to a large transformer model, reducing size while maintaining performance.

Implement causal inference methods like DoubleML or causal forests to estimate treatment effects from observational data.

Train a SimCLR model for self-supervised representation learning on image datasets, evaluating with linear probing.

Build a system for detecting anomalies in real-time data streams using online learning algorithms and Kafka integration.