For this project, students will create a predictive model to guess things like house prices, stock market trends, or how many customers will leave. The project includes gathering and cleaning data, dealing with missing values, creating features, and using machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees, and Random Forest. Participants will use metrics like accuracy, precision, recall, and F1-score to judge how well the model works. At the end of this project, students will have a working predictive model that they can look at and study to learn more about how things work in the real world.

The main goal of this project is to make a deep learning model that can sort images into different groups, like animals, handwritten numbers, or things. Using TensorFlow or Keras, students will build Convolutional Neural Networks (CNN) that use techniques like pooling, dropout, and activation functions. To make the model more general, data augmentation will be used. The result will be a trained CNN that can correctly sort images, which shows how powerful deep learning can be for computer vision tasks.

In this project, learners will create a Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM) model to process and analyze sequential text data. The project can involve sentiment analysis to determine whether text is positive, negative, or neutral, or text generation to produce coherent sequences based on a given dataset. Techniques such as tokenization, embedding layers, and sequence modeling will be applied using Python NLP libraries. By the end of the project, participants will have a model that can analyze sentiments or generate text, showcasing practical applications of deep learning in natural language processing.