GitHub - anthonyng2/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.

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Name		Name	Last commit message	Last commit date
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
04-02-MNIST(openml).ipynb		04-02-MNIST(openml).ipynb
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

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