Advanced Machine Learning Projects

These advanced machine learning projects focus on building and training deep learning models and processing unstructured datasets. You will train convolutional neural networks, gated recurrent units, finetune large language models, and reinforcement learning models.

11. Build Rick Sanchez Bot Using Transformers

In the Build Rick Sanchez Bot Using Transformers project, you will use DialoGPT and the Hugging Face Transformer library to build your AI-powered chatbot.

You will process and transform your data, build and finetune Microsoft’s Large-scale Pretrained Response Generation Model (DialoGPT) on Rick and Morty dialogues dataset. You can also create a simple Gradio app to test your model in real-time: Rick & Morty Block Party.

12. Building an E-Commerce Clothing Classifier Model with Keras

The Building an E-Commerce Clothing Classifier Model with Keras project focuses on image classification in the context of e-commerce. You will use Keras to build a machine learning model that automates clothing classification based on images. This is relevant for improving the shopping experience by helping customers find products faster and streamlining inventory management. Accurate classification also supports personalized recommendations, boosting customer engagement and sales.

13. Detect Traffic Signs with Deep Learning

In the Detect Traffic Signs with Deep Learning project, you will use Keras to develop a deep learning model capable of detecting traffic signs, such as stop signs and traffic lights. This technology is critical for autonomous vehicles, where quick and accurate recognition of road signals is essential for safe navigation. This project lays the groundwork for developing more advanced, safe, and reliable self-driving vehicle systems.

14. Stock Market Analysis And Forecasting Using Deep Learning

In the Stock Market Analysis And Forecasting project, you will use GRUs (Gated Recurrent Unit) to build deep learning forecasting models for predicting stock prices of Amazon, IBM, and Microsoft.

In the first part, you will dive deep into times series analytics to learn about trends and seasonality of stock price, and then you will use this information to process your data and build a GRU model using PyTorch. For guidance, you can check out the code source on GitHub.

Image from Soham Nandi

15. Reinforcement Learning for Connect X

Connect X is a simulation competition by Kaggle. Build an RL (Reinforcement Learning) agent to compete against other Kaggle competition participants.

You will first learn how the game works and create a dummy functional agent for a baseline. After that, you will start experimenting with various RL algorithms and model architectures. You can try building a model on Deep Q-learning or Proximal Policy Optimization algorithm.