ML: Hands-On Projects

Hands-On Projects

This module introduces hands-on portfolio projects that reinforce key machine learning concepts through practical implementation. Each project focuses on a real-world problem and guides learners through data preprocessing, model development, evaluation, and deployment.

Project 1: Customer Churn Prediction#

Objective: Build a machine learning model to predict customer churn based on historical data.

• Perform data preprocessing and feature engineering.
• Train and evaluate a classification model.
• Deploy the model as an API using Flask or FastAPI.

Project 2: Image Classification with CNNs#

Objective: Develop a deep learning model to classify images using Convolutional Neural Networks (CNNs).

• Build a CNN for image classification.
• Apply data augmentation techniques to improve model performance.
• Fine-tune a pre-trained model (e.g., VGG16, ResNet, EfficientNet).

Project 3: Sentiment Analysis with NLP#

Objective: Implement a Natural Language Processing (NLP) model to analyze sentiment in text data.

• Preprocess text data for sentiment classification.
• Train deep learning models such as LSTMs and Transformer-based models.
• Deploy the NLP model as a REST API for real-time analysis.

Project 4: Stock Market Prediction with Time Series Analysis#

Objective: Use machine learning techniques to forecast stock prices based on historical trends.

• Implement ARIMA, LSTMs, and XGBoost models for financial forecasting.
• Compare model performance using RMSE and MAE.
• Visualize forecasting results using Matplotlib and Seaborn.

Each project will have a dedicated module detailing step-by-step implementation, best practices, and deployment strategies.