GitHub - halber/udemy-the-complete-machine-learning-course-with-python: Please see the readme file for more info

Corresponding files to go with The Complete Machine Learning Course with Python course.

Navigation Menu

Repository files navigation

About

Releases

Packages

Contributors

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 79 Commits
0204 - Navigating the Spyder & Jupyter Notebook Interface.zip		0204 - Navigating the Spyder & Jupyter Notebook Interface.zip
0205 - Downloading the IRIS Datasets.zip		0205 - Downloading the IRIS Datasets.zip
0206 - Data Exploration and Analysis.zip		0206 - Data Exploration and Analysis.zip
0207 - Presenting Your Data.zip		0207 - Presenting Your Data.zip
0303 - Working with Scikit-Learn.zip		0303 - Working with Scikit-Learn.zip
0304 - Boston Housing Data - EDA.zip		0304 - Boston Housing Data - EDA.zip
0305 - Correlation Analysis and Feature Selection.zip		0305 - Correlation Analysis and Feature Selection.zip
0306 - Simple Linear Regression Modelling with Boston Housing Data.zip		0306 - Simple Linear Regression Modelling with Boston Housing Data.zip
0307 - Robust Regression.zip		0307 - Robust Regression.zip
0308 - Evaluate Model Performance.zip		0308 - Evaluate Model Performance.zip
0309 - Multiple Regression with statsmodel.zip		0309 - Multiple Regression with statsmodel.zip
0310 - Multiple Regression and Feature Importance.zip		0310 - Multiple Regression and Feature Importance.zip
0311 - Ordinary Least Square Regression and Gradient Descent.zip		0311 - Ordinary Least Square Regression and Gradient Descent.zip
0312 - Regularised Method for Regression.zip		0312 - Regularised Method for Regression.zip
0313 - Polynomial Regression.zip		0313 - Polynomial Regression.zip
0314 - Dealing with Non-linear relationships.zip		0314 - Dealing with Non-linear relationships.zip
0315 - Feature Importance Revisited.zip		0315 - Feature Importance Revisited.zip
0316 - Data Pre-Processing 1.zip		0316 - Data Pre-Processing 1.zip
0317 - Data Pre-Processing 2.zip		0317 - Data Pre-Processing 2.zip
0318 - Variance Bias Trade Off - Validation Curve.zip		0318 - Variance Bias Trade Off - Validation Curve.zip
0319 - Variance Bias Trade Off - Learning Curve.zip		0319 - Variance Bias Trade Off - Learning Curve.zip
0320 - Cross Validation.zip		0320 - Cross Validation.zip
0403 - Logistic Regression 1.zip		0403 - Logistic Regression 1.zip
0404 - Logistic Regression 2.zip		0404 - Logistic Regression 2.zip
0405 - MNIST Project 1 - Introduction.zip		0405 - MNIST Project 1 - Introduction.zip
0406 - MNIST Project 2 - SGDClassifier.zip		0406 - MNIST Project 2 - SGDClassifier.zip
0407 - MNIST Project 3 - Performance Measures.zip		0407 - MNIST Project 3 - Performance Measures.zip
0408 - MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score.zip		0408 - MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score.zip
0409 - MNIST Project 5 - Precision and Recall Tradeoff.zip		0409 - MNIST Project 5 - Precision and Recall Tradeoff.zip
0410 - MNIST Project 6 - The ROC Curve.zip		0410 - MNIST Project 6 - The ROC Curve.zip
0502 - Linear SVM Classification.zip		0502 - Linear SVM Classification.zip
0503 - Polynomial Kernel.zip		0503 - Polynomial Kernel.zip
0504 - Gaussian Radial Basis Function.zip		0504 - Gaussian Radial Basis Function.zip
0505 - Support Vector Regression.zip		0505 - Support Vector Regression.zip
0506 - Advantage and Disadvantages of SVM.zip		0506 - Advantage and Disadvantages of SVM.zip
0602 - What is Decision Tree.zip		0602 - What is Decision Tree.zip
0603 - Training a Decision Tree.zip		0603 - Training a Decision Tree.zip
0604 - Visualising a Decision Trees.zip		0604 - Visualising a Decision Trees.zip
0605 - Decision Tree Learning Algorithm.zip		0605 - Decision Tree Learning Algorithm.zip
0606 - Decision Tree Regression.zip		0606 - Decision Tree Regression.zip
0607 - Overfitting and Grid Search.zip		0607 - Overfitting and Grid Search.zip
0608 - Where to From Here.zip		0608 - Where to From Here.zip
0609 - Project HR - Loading and preprocesing data.zip		0609 - Project HR - Loading and preprocesing data.zip
0610 - Project HR - Modelling.zip		0610 - Project HR - Modelling.zip
0702 - Ensemble Learning Methods Introduction.zip		0702 - Ensemble Learning Methods Introduction.zip
0703 - Bagging Part 1.zip		0703 - Bagging Part 1.zip
0704 - Bagging Part 2.zip		0704 - Bagging Part 2.zip
0705 - Random Forests.zip		0705 - Random Forests.zip
0706 - Extra-Trees.zip		0706 - Extra-Trees.zip
0707 - AdaBoost.zip		0707 - AdaBoost.zip
0708 - Gradient Boosting Machine.zip		0708 - Gradient Boosting Machine.zip
0709 - XGBoost.zip		0709 - XGBoost.zip
0710 - Project HR - Human Resources Analytics.zip		0710 - Project HR - Human Resources Analytics.zip
0711 - Ensemble of ensembles 1.zip		0711 - Ensemble of ensembles 1.zip
0712 - Ensemble of ensembles 2.zip		0712 - Ensemble of ensembles 2.zip
0802 - k-Nearest Neighbour Concepts.zip		0802 - k-Nearest Neighbour Concepts.zip
0803 - kNN and IRIS demo.zip		0803 - kNN and IRIS demo.zip
0805 - Project Cancer Detection 1.zip		0805 - Project Cancer Detection 1.zip
0806 - Project Cancer Detection 2.zip		0806 - Project Cancer Detection 2.zip
0902 - Dimensionality Reduction Concept .zip		0902 - Dimensionality Reduction Concept .zip
0903 - PCA Introduction .zip		0903 - PCA Introduction .zip
0906 - Project Wine 2 Choosing the Number of Components .zip		0906 - Project Wine 2 Choosing the Number of Components .zip
0907 - Kernel PCA .zip		0907 - Kernel PCA .zip
0908 - Kernel PCA Demo .zip		0908 - Kernel PCA Demo .zip
0909 - LDA & Comparison between LDA and PCA .zip		0909 - LDA & Comparison between LDA and PCA .zip
1002 - Clustering.zip		1002 - Clustering.zip
Code snippets.ipynb		Code snippets.ipynb
README.md		README.md

Folders and files

Latest commit

History

Repository files navigation

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages