GitHub - TirendazAcademy/Machine-Learning-Tutorial: Machine Learning Tutorial

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Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Datasets		Datasets
Images		Images
.gitattributes		.gitattributes
01-How To Build a Machine Learning Model.ipynb		01-How To Build a Machine Learning Model.ipynb
02-Scikit-Learn Part 1.ipynb		02-Scikit-Learn Part 1.ipynb
03-Scikit-Learn Part 2.ipynb		03-Scikit-Learn Part 2.ipynb
04-FEATURE ENGINEERING.ipynb		04-FEATURE ENGINEERING.ipynb
05-K Nearest Neighbor (KNN) Algorithm.ipynb		05-K Nearest Neighbor (KNN) Algorithm.ipynb
06-LINEAR REGRESSION.ipynb		06-LINEAR REGRESSION.ipynb
07-LINEAR REGRESSION in ACTION.ipynb		07-LINEAR REGRESSION in ACTION.ipynb
08-Ridge & Lasso Regression.ipynb		08-Ridge & Lasso Regression.ipynb
09-LINEAR MODELS FOR CLASSIFICATION.ipynb		09-LINEAR MODELS FOR CLASSIFICATION.ipynb
10-LOGISTIC REGRESSION in ACTION.ipynb		10-LOGISTIC REGRESSION in ACTION.ipynb
11-NAIVE BAYES CLASSIFICATION.ipynb		11-NAIVE BAYES CLASSIFICATION.ipynb
12-SUPPORT VECTOR MACHINES.ipynb		12-SUPPORT VECTOR MACHINES.ipynb
13-DECISION TREES.ipynb		13-DECISION TREES.ipynb
14-ENSEMBLE LEARNING.ipynb		14-ENSEMBLE LEARNING.ipynb
15-ARTIFICIAL NEURAL NETWORK.ipynb		15-ARTIFICIAL NEURAL NETWORK.ipynb
16-Unsupervised Learning & Data Scaling.ipynb		16-Unsupervised Learning & Data Scaling.ipynb
17-Principal Component Analysis (PCA).ipynb		17-Principal Component Analysis (PCA).ipynb
18-MANIFOLD LEARNING.ipynb		18-MANIFOLD LEARNING.ipynb
19-K Means Clustering.ipynb		19-K Means Clustering.ipynb
20-Agglomerative & Hierarchica & DBSCAN Clustering.ipynb		20-Agglomerative & Hierarchica & DBSCAN Clustering.ipynb
21-Gaussion Mixture Models.ipynb		21-Gaussion Mixture Models.ipynb
22-MODEL EVALUATION.ipynb		22-MODEL EVALUATION.ipynb
23-MODEL IMPROVEMENT.ipynb		23-MODEL IMPROVEMENT.ipynb
24-Evaluation Metrics .ipynb		24-Evaluation Metrics .ipynb
25-PIPELINES.ipynb		25-PIPELINES.ipynb
26-TEXT ANALYSIS.ipynb		26-TEXT ANALYSIS.ipynb
27-FEATURE ENGINEERING.ipynb		27-FEATURE ENGINEERING.ipynb
28-FEATURE SELECTION.ipynb		28-FEATURE SELECTION.ipynb
29-INTRODUCTION MACHINE LEARNING.ipynb		29-INTRODUCTION MACHINE LEARNING.ipynb
30-LINEAR REGRESSION FROM SCRATCH.ipynb		30-LINEAR REGRESSION FROM SCRATCH.ipynb
31-Data Preprocessing - Part 1.ipynb		31-Data Preprocessing - Part 1.ipynb
32-Data Preprocessing - Part 2.ipynb		32-Data Preprocessing - Part 2.ipynb
33-Data Scaling & Regularization & Feature Engineering.ipynb		33-Data Scaling & Regularization & Feature Engineering.ipynb
34-Introduction to Scikit-Learn.ipynb		34-Introduction to Scikit-Learn.ipynb
35-Machine Learning with PyCaret.ipynb		35-Machine Learning with PyCaret.ipynb
36-Housing Prices Prediction with Random Forest.ipynb		36-Housing Prices Prediction with Random Forest.ipynb
Machine-Learning-101.ipynb		Machine-Learning-101.ipynb
README.md		README.md

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Welcome to the Machine Learning with Python repository! This repository is designed to be a comprehensive resource for anyone looking to learn about machine learning using Python.

This repo contains the jupyter notebooks and datasets I used on the Tirendaz Akademi Youtube channel. In these tutorials, I used Pandas for data preprocessing, Numpy for multidimensional arrays and matrix operations, Matplotlib and Seaborn for data visualization, Scikit-Learn to implement machine learning algorithms. Whether you're a beginner or an experienced programmer, this repo has something for everyone.

The repo is organized into different sections, each focusing on a specific topic related to machine learning. You'll find sections on data preprocessing, regression, classification, clustering, and more. Each section contains a variety of resources, including Jupyter notebooks, Python scripts, and links to external resources.

Whether you're looking to build predictive models, perform data analysis, or just learn more about machine learning, this repo has everything you need to get started. So dive in and start learning today!

Let me know if you have any questions. If you enjoy these notebooks and videos, don't forget to give a star ✨.

Happy learning ... ✌️

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