These are my notes for the above course.

Please feel free to email me on [email protected]

Below is the Table of Contents and the accompanying codes

• What does the Course Cover?

• Downloading & Installing Anaconda
• Managing Environment
• Navigating the Spyder & Jupyter Notebook Interface
• Downloading the IRIS Datasets
• Data Exploration and Analysis & Presenting Your Data

• Introduction
• Categories of Machine Learning
• Working with Scikit-Learn

Codes for the following Boston Housing section

• Boston Housing Data - EDA
• Boston Housing Data - Correlation Analysis and Feature Selection
• Boston Housing Data - Simple Linear Regression Modelling with Boston Housing Data
• Boston Housing Data - Robust Regression
• Boston Housing Data - Evaluate Model Performance
• Multiple Regression with statsmodel and Feature Importance
• Ordinary Least Square Regression and Gradient Descent
• Regularised Method for Regression
• Polynomial Regression
• Dealing with Non-linear relationships & Feature Importance Revisited
• Data Pre-processing
• Variance Bias Trade Off - Validation Curve & Learning Curve
• Cross Validation

• Introduction
• Logistic Regression

Codes for the MNIST Project

• MNIST Project - SGDClassifier
• MNIST Project - Performance Measures
• MNIST Project - Confusion Matrix
• MNIST Project - Precision, Recall and F1 Score
• MNIST Project - Precision and Recall Tradeoff
• MNIST Project - The ROC Curve

Topics covered and codes

• Introduction
• Support Vector Machine (SVM) Concepts
• Linear SVM Classification
• Polynomial Kernel
• Gaussian Radial Basis Function
• Support Vector Regression
• Advantages and Disadvantages of SVM

Topics covered and the codes

• Introduction
• What is Decision Tree
• Training a Decision Tree
• Visualising a Decision Trees
• Decision Tree Learning Algorithm
• Decision Tree Regression
• Overfitting and Grid Search
• Where to From Here
• Project HR - Loading and preprocesing data & Modelling

• Introduction
• Ensemble Learning Methods Introduction
• Bagging Part 1 & Part 2
• Random Forests & Extra-Trees
• AdaBoost & Gradient Boosting Machine
• XGBoost Installation Guide. NB: Do not forget to set up an environment for this
• XGBoost
• Project HR - Human Resources Analytics
• Ensemble of ensembles 1
• Ensemble of ensembles 1

Topics covered and the codes

• kNN Introduction
• kNN Concepts
• kNN and Iris Dataset Demo
• Distance Metric
• Project Cancer Detection

• Introduction
• Dimensionality Reduction Concept
• PCA Introduction
• Dimensionality Reduction Demo
• Project Wine 1: Dimensionality Reduction with PCA
• Project Wine 2: Choosing the Number of Components
• Kernel PCA
• Kernel PCA Demo
• LDA & Comparison between LDA and PCA
• Project Abalone

• Clustering Introduction
• Overview of Clustering Methods
• Installing Mlxtend
• Ward’s Agglomerative Hierarchical Clustering
• Truncating Dendrogram
• k-Means Clustering
• Elbow Method
• Silhouette Analysis
• Mean Shift

• DeepFake