I will be using this repo for my ML course
Assignment 1 : - This assignment deals with the most recent Airbnb listings in Copenhagen. The data is collected from Inside Airbnb.
Assignment 2 : - Utilize data from the K2 Kepler mission (which concluded in 2018) to develop a machine learning model that assists in classifying celestial bodies and determining their exoplanet status. An exoplanet is defined as "A planet that orbits a star outside the solar system".
Assignment 3 : - The purpose of the assignment is to assess whether regression could be used instead of neural networks, which could potentially save time and money as it is generally quicker to perform a regression than to train a neural network. And the main objective is thus to develop a regression model that can generate LTC time series that are both accurate and cost-effective.
Assignment 4 :- This exercise focuses on the candidate tests from two television networks: DR and TV2. Data from both tests have been given on a scale of five responses (-2, -1, 0, 1, 2). Consider normalizing the data or performing similar scaling transformations as needed.
Assignment 5 :- In this exercise we use the IMDb-dataset, which we will use to perform a sentiment analysis. The code below assumes that the data is placed in the same folder as this notebook. We see that the reviews are loaded as a pandas dataframe, and print the beginning of the first few reviews.
Assingment 6 :- In this exercise, we will work with the Google Speech Command Dataset, which can be downloaded from here (note: you do not need to download the full dataset, but it will allow you to play around with the raw audiofiles). This dataset contains 105,829 one-second long audio files with utterances of 35 common words.
Assignemmt 7 (Group Project ): - Almost everyone today uses technology to stream movies and television shows. While figuring out what to stream next can be daunting, recommendations are often made based on a viewer’s history and preferences. So Our goal is to build a recommendation system that recommends movies to the users based on their previous ratings for other movies. We will be using the MovieLens dataset to build a recommendation system using the collaborative filtering technique.