Introduction
  • What Does the Course Cover?
  • How to Succeed in This Course
  • Project Files and Resources
Getting Started with Anaconda
  • Installing Applications and Creating Environment
  • Hello World
  • Iris Project 1: Working with Error Messages
  • Iris Project 2: Reading CSV Data into Memory
  • Iris Project 3: Loading data from Seaborn
  • Iris Project 4: Visualization
Regression
  • Scikit-Learn
  • EDA
  • Correlation Analysis and Feature Selection
  • Correlation Analysis and Feature Selection
  • Linear Regression with Scikit-Learn
  • Five Steps Machine Learning Process
  • Robust Regression
  • Evaluate Regression Model Performance
  • Multiple Regression 1
  • Multiple Regression 2
  • Regularized Regression
  • Polynomial Regression
  • Dealing with Non-linear Relationships
  • Feature Importance
  • Data Preprocessing
  • Variance-Bias Trade Off
  • Learning Curve
  • Cross Validation
  • CV Illustration
Classification
  • Logistic Regression
  • Introduction to Classification
  • Understanding MNIST
  • SGD
  • Performance Measure and Stratified k-Fold
  • Confusion Matrix
  • Precision
  • Recall
  • f1
  • Precision Recall Tradeoff
  • Altering the Precision Recall Tradeoff
  • ROC
Support Vector Machine (SVM)
  • Support Vector Machine (SVM) Concepts
  • Linear SVM Classification
  • Polynomial Kernel
  • Radial Basis Function
  • Support Vector Regression
Tree
  • Introduction to Decision Tree
  • Training and Visualizing a Decision Tree
  • Visualizing Boundary
  • Tree Regression, Regularization and Over Fitting
  • End to End Modeling
  • Project HR
  • Project HR with Google Colab
Ensemble Machine Learning
  • Ensemble Learning Methods Introduction
  • Bagging
  • Random Forests and Extra-Trees
  • AdaBoost
  • Gradient Boosting Machine
  • XGBoost Installation
  • XGBoost
  • Project HR - Human Resources Analytics
  • Ensemble of Ensembles Part 1
  • Ensemble of ensembles Part 2
k-Nearest Neighbours (kNN)
  • kNN Introduction
  • Project Cancer Detection
  • Addition Materials
  • Project Cancer Detection Part 1
Unsupervised Learning: Dimensionality Reduction
  • Dimensionality Reduction Concept
  • PCA Introduction
  • Project Wine
  • Kernel PCA
  • Kernel PCA Demo
  • LDA vs PCA
  • Project Abalone
Unsupervised Learning: Clustering
  • Clustering
  • k_Means Clustering
Deep Learning
  • Estimating Simple Function with Neural Networks
  • Neural Network Architecture
  • Motivational Example - Project MNIST
  • Binary Classification Problem
  • Natural Language Processing - Binary Classification
Appendix A1: Foundations of Deep Learning
  • Introduction to Neural Networks
  • Differences between Classical Programming and Machine Learning
  • Learning Representations
  • What is Deep Learning
  • Learning Neural Networks
  • Why Now?
  • Building Block Introduction
  • Tensors