This repository contains all the practices carried out during my data analysis and artificial intelligence course. The content is divided into blocks, and each block is further divided into sessions.
- About
- Table of Contents
- Block 1: Introduction to Python
- Block 2: Regression
- Block 3: Classification
- Block 4: Unsupervised Learning
- Block 5: Time Series
- Usage
- License
- Authors
This repository contains all the practical exercises and projects undertaken during my Data Analysis and Artificial Intelligence course. The repository is organized into blocks, with each block further divided into sessions. Each session includes a Jupyter notebook to facilitate hands-on learning and experimentation.
Introduction to the basics of Python programming.
Understanding structures and control functions in Python.
Handling data, arrays, and matrices in Python.
Working with DataFrames and Series in Python.
Performing descriptive analysis.
Introduction to linear regression.
Understanding regularization techniques.
Using decision trees for regression.
Applying random forest for regression tasks.
Using neural networks for regression.
Summary and conclusion of the regression block.
Introduction to logistic classification.
Classifying variables.
Classification using DR and RF techniques.
Using Bayes, KNN, and SVM for classification.
Applying neural networks for classification.
Working with convolutional neural networks.
Summary and conclusion of the classification block.
Introduction to unsupervised learning techniques.
Understanding Principal Component Analysis (PCA).
Project on mine detection and lesson on centroid-based clustering.
Exploring other clustering methods.
Summary and conclusion of the clustering block.
Introduction to time series analysis.
Analyzing time series data.
Using statistical models for analysis.
Machine learning forecasting techniques.
Summary and conclusion of the time series block.
Each session is organized in its own folder, and each session includes a Jupyter notebook. You can run the notebooks to follow along with the course content.
This project is licensed under the MIT License.
This project is maintained by @pablosalme.