02 Getting Started with Anaconda

  attached_files

   008 Navigating the Spyder Jupyter Notebook Interface

   009 Downloading the IRIS Datasets

   010 Data Exploration and Analysis

   011 Presenting Your Data

  quizzes

  005 Windows OS Managing Environment.mp4

  007 Practice Activity Create a New Environment.html

  009 Downloading the IRIS Datasets.mp4

  011 Presenting Your Data.mp4

 03 Regression

  attached_files

   014 Working with Scikit-Learn

   015 Boston Housing Data - EDA

   016 Correlation Analysis and Feature Selection

   017 Simple Linear Regression Modelling with Boston Housing Data

   018 Robust Regression

   019 Evaluate Model Performance

   020 Multiple Regression with statsmodel

   021 Multiple Regression and Feature Importance

   022 Ordinary Least Square Regression and Gradient Descent

   023 Regularised Method for Regression

   024 Polynomial Regression

   025 Dealing with Non-linear relationships

   026 Feature Importance Revisited

   027 Data Pre-Processing 1

   028 Data Pre-Processing 2

   029 Variance Bias Trade Off - Validation Curve

   030 Variance Bias Trade Off - Learning Curve

   031 Cross Validation

  quizzes

   003 Section 3.html

  013 Categories of Machine Learning.mp4

  015 Boston Housing Data - EDA.mp4

  017 Simple Linear Regression Modelling with Boston Housing Data.mp4

  019 Evaluate Model Performance.mp4

  021 Multiple Regression and Feature Importance.mp4

  023 Regularised Method for Regression.mp4

  025 Dealing with Non-linear relationships.mp4

  027 Data Pre-Processing 1.mp4

  029 Variance Bias Trade Off - Validation Curve.mp4

  031 Cross Validation.mp4

 04 Classification

  attached_files

   033 Logistic Regression 1

   034 Logistic Regression 2

   035 MNIST Project 1 - Introduction

   036 MNIST Project 2 - SGDClassifier

   037 MNIST Project 3 - Performance Measures

   038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score

   039 MNIST Project 5 - Precision and Recall Tradeoff

   040 MNIST Project 6 - The ROC Curve

  032 Introduction.mp4

  034 Logistic Regression 2.mp4

  036 MNIST Project 2 - SGDClassifier.mp4

  038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score.mp4

  040 MNIST Project 6 - The ROC Curve.mp4

 05 Support Vector Machine SVM

  attached_files

   043 Support Vector Machine SVM Concepts

   044 Linear SVM Classification

   045 Polynomial Kernel

   046 Gaussian Radial Basis Function

   047 Support Vector Regression

  042 Introduction.mp4

  044 Linear SVM Classification.mp4

  046 Gaussian Radial Basis Function.mp4

  048 Advantages and Disadvantages of SVM.mp4

 06 Tree

  attached_files

   050 What is Decision Tree

   051 Training a Decision Tree

   052 Visualising a Decision Trees

   053 Decision Tree Learning Algorithm

   054 Decision Tree Regression

   055 Overfitting and Grid Search

   056 Where to From Here

   057 Project HR - Loading and preprocesing data

   058 Project HR - Modelling

  049 Introduction.mp4

  051 Training a Decision Tree.mp4

  053 Decision Tree Learning Algorithm.mp4

  055 Overfitting and Grid Search.mp4

  057 Project HR - Loading and preprocesing data.mp4

 07 Ensemble Machine Learning

  attached_files

   060 Ensemble Learning Methods Introduction

   061 Bagging Part 1

   062 Bagging Part 2

   063 Random Forests

   064 Extra-Trees

   065 AdaBoost

   066 Gradient Boosting Machine

   067 XGBoost

   068 Project HR - Human Resources Analytics

   069 Ensemble of ensembles Part 1

   070 Ensemble of ensembles Part 2

  059 Introduction.mp4

  061 Bagging Part 1.mp4

  063 Random Forests.mp4

  065 AdaBoost.mp4

  067 XGBoost.mp4

  069 Ensemble of ensembles Part 1.mp4

 08 k-Nearest Neighbours kNN

  attached_files

   072 kNN Concepts

   073 kNN and Iris Dataset Demo

   075 Project Cancer Detection Part 1

   076 Project Cancer Detection Part 2

  071 kNN Introduction.mp4

  073 kNN and Iris Dataset Demo.mp4

  075 Project Cancer Detection Part 1.mp4

 09 Dimensionality Reduction

  attached_files

   078 Dimensionality Reduction Concept

   079 PCA Introduction

   082 Project Abalone

   083 Project Wine 2 Choosing the Number of Components

   084 Kernel PCA

   085 Kernel PCA Demo

   086 LDA Comparison between LDA and PCA

  077 Introduction.mp4

  079 PCA Introduction.mp4

  081 Project Wine 1 Dimensionality Reduction with PCA.mp4

  083 Project Wine 2 Choosing the Number of Components.mp4

  085 Kernel PCA Demo.mp4

 10 Unsupervised Learning Clustering

  attached_files

   088 Clustering Concepts

   089 MLextend

   090 Wards Agglomerative Hierarchical Clustering

   091 Truncating Dendrogram

   092 k-Means Clustering

   093 Elbow Method

   094 Silhouette Analysis

   095 Mean Shift

  087 Introduction.mp4

  089 MLextend.mp4

  091 Truncating Dendrogram.mp4

  093 Elbow Method.mp4

  095 Mean Shift.mp4

 01 Introduction

  002 How to Succeed in This Course.html

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