1.
Apply the bag-of-words model to the IMDb movie review dataset to convert textual data
into numerical features suitable for machine learning.
2. Apply a logistic regression model to the IMDb movie review dataset and classify reviews
as positive or negative using the bag-of-words representation.
3. Apply Flask to develop a web application that integrates the movie classifier, allowing
users to input movie reviews and receive sentiment predictions.
4. Apply deployment strategies to host the Flask-based movie classifier web application on
a public server, making it accessible to users.
5. Apply polynomial regression to extend a linear regression model into a curve and
compare its performance against standard linear regression using the Housing Dataset
6. Apply a simple linear regression model to the Housing Dataset to predict housing prices
and evaluate its performance using appropriate metrics.
7. Develop a k-means clustering algorithm to group objects by similarity and analyze the
resulting clusters.
8. Develop a hierarchical clustering model to organize objects into a tree structure based on
their similarities.
9. Develop an artificial neural network to model complex functions and classify
handwritten digits with high accuracy.
10. Develop and experiment with different neural network architectures to improve
performance on handwritten digit classification tasks.
