A comprehensive collection of Machine Learning algorithms and models implemented while completing the Machine Learning A-Z™: AI, Python & R + ChatGPT Prize course on Udemy.
This repository documents my hands-on learning journey through classical Machine Learning concepts, covering everything from data preprocessing and supervised learning to deep learning. Each notebook demonstrates the complete workflow, including data preprocessing, model training, prediction, and evaluation using real-world datasets.
- Data Preprocessing Tools
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- K-Means Clustering
- Hierarchical Clustering
- Apriori Algorithm
- Eclat Algorithm
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Artificial Neural Network (ANN)
- Convolutional Neural Network (CNN)
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- TensorFlow
Throughout this repository, I gained practical experience in:
- Data preprocessing and feature scaling
- Data visualization
- Supervised Learning
- Unsupervised Learning
- Regression techniques
- Classification algorithms
- Clustering methods
- Association Rule Learning
- Reinforcement Learning fundamentals
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Model evaluation and performance analysis
This repository contains my implementations and practice notebooks completed while taking the Machine Learning A-Z™: AI, Python & R + ChatGPT Prize course on Udemy by Kirill Eremenko and Hadelin de Ponteves.
https://www.udemy.com/certificate/UC-7e01f650-480b-464b-ab82-dad0e65c2215/
The notebooks in this repository were developed as part of my learning journey while completing the above Udemy course. The implementations are based on the course curriculum and are intended for educational and portfolio purposes. This repository showcases my understanding and hands-on practice of fundamental Machine Learning algorithms rather than original research or novel algorithm implementations.
Aditya Mohan Jha
- GitHub: https://github.com/ADITYA9571
If you found this repository useful or interesting, consider giving it a ⭐ to support my work!