├── Classification ├── Advanced Scikit-Learn Classification Techniques.ipynb ├── Car Evaluation using Decision trees and Random Forests.ipynb ├── Classifiying Ionosphere structure using K nearest neigbours algorithm.ipynb ├── Detecting Faces wearing glasses with Support Vector Machines.ipynb ├── Logistic Regression for Banknote Authentication.ipynb ├── Support Vector Machines for Energy-Efficiency Classification.ipynb └── Zoo Animal Classification using Naive Bayes.ipynb ├── Clustering ├── Applying K-Means for Image Quantization.ipynb ├── Customer Segmentation for Market Analysis.ipynb └── Seeds Clustering.ipynb ├── Ensemble Learning ├── Classifying Default of Credit Card Clients.ipynb ├── Ensemble Learning to Classify Patients with Heart Disease.ipynb └── OnlineNewsPopularity Classification using Ensembles.ipynb ├── LICENSE.md ├── Machine Learning using GraphLab ├── Analyzing Product Sentiment using GraphLab Create.ipynb ├── Document Retrieval using GraphLab Create.ipynb ├── Predicting House Prices using GraphLab Create.ipynb ├── Recommender Systems using Affinity Analysis.ipynb └── Song Recommender System using GraphLab Create.ipynb ├── Maching Learning using Scikit-Learn ├── Getting-Started.ipynb └── Introduction.ipynb ├── Miscellaneous ├── Adult Income Classification.ipynb ├── Connect-4 Classification.ipynb ├── Gesture-Phase-Detection.ipynb ├── Indian-Liver-Patient-Classification.ipynb ├── Leaf Classification.ipynb ├── Lenses Data Classification.ipynb ├── Plants Clustering.ipynb ├── Student-Performance-Evaluation-Classification-Regression.ipynb ├── Tic-Tac-Toe Endgame Classification.ipynb ├── Topic Modelling using LDA.ipynb ├── modified_mnist.ipynb ├── wine_neural_network.pdf └── wine_neural_network.py ├── README.md ├── Regression ├── Advanced Scikit-Learn Regression Techniques.ipynb ├── Air-Quality-Prediction.ipynb └── Predicting Electrical Energy Output with Regression Analysis.ipynb ├── datasets ├── AirQualityUCI.csv ├── ENB2012_data.xlsx ├── Indian Liver Patient Dataset (ILPD).csv ├── Ionosphere │ ├── ionosphere.data │ └── ionosphere.names ├── OnlineNewsPopularity.csv ├── Wholesale customers data.csv ├── a1_raw.csv ├── a1_va3.csv ├── adult.data ├── adult.names ├── adult.test ├── car.data ├── connect-4.data ├── data_Mar_64.txt ├── data_Sha_64.txt ├── data_Tex_64.txt ├── data_banknote_authentication.txt ├── default of credit card clients.xls ├── leaf │ ├── ReadMe.pdf │ └── leaf.csv ├── lenses.data ├── plants.data ├── processed.cleveland.data ├── seeds_dataset.txt ├── student │ ├── student-mat.csv │ ├── student-merge.R │ ├── student-por.csv │ ├── student.pdf │ └── student.txt ├── tic-tac-toe.data └── zoo.data └── images ├── 01_08.png ├── adaboost.png ├── bagging.png ├── bayes.png ├── bias-variance.png ├── classification.png ├── clustering.png ├── cv.png ├── decisiontrees.png ├── dr.png ├── ensemble.png ├── gmplot.png ├── knn.png ├── majorityvoting.png ├── overfitting.png ├── preprocessing.png ├── regression.png ├── reinforcement.png 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