This repository contains a collection of exercises from the Introduction to Data Science course. Each exercise covers fundamental concepts in Python programming, data analysis, and machine learning. Below is an overview of the exercises and the topics they address.
- Exercise 1: Python Expressions
- Exercise 2: Web Scraping & Pandas
- Exercise 3: Data Analysis & Visualization
- Exercise 4: Data Preparation
- Exercise 5: Introduction to Machine Learning
- Exercise 6: Machine Learning
- Exercise 7: Boosting Techniques
- Exercise 8: Neural Networks & Deep Learning
- Exercise 9: Advanced Neural Networks & NLP
- Exercise 10: NLP & Transfer Learning
This exercise introduces basic Python expressions, variables, and operators. It is designed to familiarize you with how Python handles different types of data and operations.
File: Exercise_1.ipynb
In this exercise, you'll learn how to scrape data from websites using Python and then process that data using Pandas. Key skills include working with HTML, extracting data, and organizing data into dataframes for analysis.
File: Exercise_2.ipynb
This exercise covers the fundamental techniques of data analysis and data visualization. You will work with datasets to uncover patterns, generate insights, and create visual representations of your findings using libraries like Matplotlib and Seaborn.
Files:
Exercise_3_full_code.ipynbExercise_3_part1.ipynb
This exercise focuses on the steps involved in preparing data for analysis, including cleaning, transforming, and normalizing datasets.
File: Exercise_4.ipynb
This exercise provides an introduction to machine learning, covering the basic concepts of supervised and unsupervised learning. It introduces models such as linear regression and decision trees.
File: Exercise_5.ipynb
Explore more advanced machine learning techniques, focusing on building, training, and evaluating models using Python's machine learning libraries, such as Scikit-Learn.
File: Exercise_6.ipynb
This exercise dives into advanced machine learning techniques, specifically boosting methods such as AdaBoost and Gradient Boosting, which are used to improve model performance.
File: Exercise_7.ipynb
Learn about the foundations of neural networks and their applications in deep learning. This exercise introduces key concepts like feedforward networks and backpropagation.
File: Exercise_8.ipynb
This exercise covers more advanced topics in neural networks, including recurrent neural networks (RNNs) and their application in Natural Language Processing (NLP).
File: Exercise_9.ipynb
Explore the world of Natural Language Processing (NLP) and Transfer Learning in this exercise. You will learn how to work with text data, extract insights, and apply advanced machine learning techniques to analyze language.
File: Exercise_10.ipynb