How muhammadjunaid-ds/ai-using-python works
This repository is a collection of machine-learning algorithm practice notebooks, not an application runtime. The apparent user workflow is to choose an algorithm topic, study its concepts and variants, then work through implementation notebooks where provided. The graph reflects notebook titles and directory organization only; README and source excerpts do not document notebook contents or runtime relationships.
Main entry points: Decision Tree 01.ipynb Tree Concepts · Decision Tree 03 (Pre-Pruning vs Post-Pruning).ipynb Tree Pruning · Naive Bayes 01.ipynb Bayes Concepts · Random Forest 01.ipynb Forest Concepts
Components 26
Supervised Learning
- Tree Concepts
Dicesion Tree - Algorithm Practice/Decision Tree 01.ipynb - Tree Pruning
Dicesion Tree - Algorithm Practice/Decision Tree 03 (Pre-Pruning vs Post-Pruning).ipynb - Tree Implementation
Dicesion Tree - Algorithm Practice/Decision Tree 04 - Python Implementation From Scratch Using Scikit-learn.ipynb - Linear Regression
Linear Regression - Algorithm Practice/Untitled.ipynb - Bayes Concepts
Naive Bayes' - Algorithm Practice/Naive Bayes 01.ipynb - Bayes Implementation
Naive Bayes' - Algorithm Practice/Naive Bayes 02 - Implementation Using Scikit-learn.ipynb - Forest Concepts
Random Forest - Algorithm Practice/Random Forest 01.ipynb - Feature Importance
Random Forest - Algorithm Practice/Random Forest 03 - Feature Importance and OOB Error.ipynb - Forest Tuning
Random Forest - Algorithm Practice/Random Forest 04 - HyperParameters Tuning.ipynb - Forest Implementation
Random Forest - Algorithm Practice/Random Forest 05 - Implementation Using Scikit-learn.ipynb - SVM Concepts
SVM - Algorithm Practice/SVM 01.ipynb - SVM Kernels
SVM - Algorithm Practice/SVM 02 - Kernels.ipynb - SVM Features
SVM - Algorithm Practice/SVM 03 - High Dimension and Important Feature Handling.ipynb - SVM Implementation
SVM - Algorithm Practice/SVM 04 - Implementation Using Scikit-Learn.ipynb - k-NN Concepts
k-NN - Algorithm Practice/k-NN 01.ipynb - k-NN Optimization
k-NN - Algorithm Practice/k-NN 02 - Performance Optimization Techniques.ipynb - k-NN Implementation
k-NN - Algorithm Practice/k-NN 03 - Implementation Using Scikit-learn.ipynb
Unsupervised Learning
- K-means Concepts
Unsupervised Algorithms/KMeans Clustering - Algorithm Practice/KMeans Clustering 01.ipynb - K-means Implementation
Unsupervised Algorithms/KMeans Clustering - Algorithm Practice/KMeans Clustering 03 - Implementation Using Scikit-learn - Part l.ipynb - Hierarchy Concepts
Unsupervised Algorithms/Hierarchical Clustering - Algorithm Practice/Hierarchical Clustering 01.ipynb - Dendrograms
Unsupervised Algorithms/Hierarchical Clustering - Algorithm Practice/Hierarchical Clustering 02- Dendrograms & Cluster Selection.ipynb - Hierarchy Implementation
Unsupervised Algorithms/Hierarchical Clustering - Algorithm Practice/Hierarchical Clustering 03 - Implementation Using Scikit-learn.ipynb - PCA Mathematics
Unsupervised Algorithms/Principle Component Analysis - PCA/PCA 01 - EigenVectors and EigenValues.ipynb - Dimensionality Reduction
Unsupervised Algorithms/Principle Component Analysis - PCA/PCA 02 - Dimensionality Reduction Concept.ipynb - PCA Implementation
Unsupervised Algorithms/Principle Component Analysis - PCA/PCA 03 - Implementation with Scikit-Learn - Part I.ipynb
Other components
- Learner
Connections 25
- Learner → Tree Concepts: studies
- Tree Concepts → Tree Pruning: continues with
- Tree Pruning → Tree Implementation: continues with
- Learner → Linear Regression: studies
- Learner → Bayes Concepts: studies
- Bayes Concepts → Bayes Implementation: continues with
- Learner → Forest Concepts: studies
- Forest Concepts → Feature Importance: continues with
- Feature Importance → Forest Tuning: continues with
- Forest Tuning → Forest Implementation: continues with
- Learner → SVM Concepts: studies
- SVM Concepts → SVM Kernels: continues with
- SVM Kernels → SVM Features: continues with
- SVM Features → SVM Implementation: continues with
- Learner → k-NN Concepts: studies
- k-NN Concepts → k-NN Optimization: continues with
- k-NN Optimization → k-NN Implementation: continues with
- Learner → K-means Concepts: studies
- K-means Concepts → K-means Implementation: continues with
- Learner → Hierarchy Concepts: studies
- Hierarchy Concepts → Dendrograms: continues with
- Dendrograms → Hierarchy Implementation: continues with
- Learner → PCA Mathematics: studies
- PCA Mathematics → Dimensionality Reduction: continues with
- Dimensionality Reduction → PCA Implementation: continues with