ml-ai-bigdata-exercises

This repository contains a collection of exercises on Machine Learning (ML), Artificial Intelligence (AI), and Big Data. The exercises are organized into separate folders and cover various topics.

Exercises in the repository

• Programming language: Python

01-python-for-data-science-basics

This exercise covers the basics of Python for Data Science, focusing on Python and NumPy fundamentals.

• Tools: Jupyter Notebook, NumPy
• Content: Basic Python programming, array operations, data manipulation with NumPy

02-descriptive-statistics

This exercise covers descriptive statistics to summarize and describe the main features of a dataset. It includes calculating measures of central tendency, variability, and distribution shapes.

• Tools: Jupyter Notebook, Pandas, NumPy
• Content: Mean, median, mode, variance, standard deviation, skewness, kurtosis

03-exploratory-data-analysis

This exercise focuses on exploratory data analysis (EDA) to understand a dataset and gain initial insights. Various visualization techniques are used to better interpret the data.

• Tools: Jupyter Notebook, Pandas, Matplotlib, Seaborn
• Content: Data preprocessing, visualization of distributions, relationships, and outliers

Upcoming Exercises

The following exercises are currently under development and will be added soon:

• 04-classification
• 05-regression
• 06-boosting