Introduction to Machine Learning
  • What is Machine Learning?
  • Types of Machine Learning
  • Supervised Learning
  • Quiz 1
Optional: Setup Environment
  • Installing Anaconda
  • How to Use Spyder Notebook
  • How to use Jupiter Notebook
  • Installing Library
Optional: Setup Environment on cloud (AWS)
  • Why AWS?
  • Creating EC2 instance
  • Connect to EC2 instance
  • Installing Packages
  • Transferring Files to AWS EC2 instance
Data Preprocessing
  • What is Data Preprocessing?
  • Checking for Null Values: Concept + Python Code
  • Correlated Feature Check: Concept + Python Code
  • Data Molding(Encoding): Concept + Python Code
  • Impute Missing Values: Concept + Python Code
  • Scaling
  • Scaling: Python Code
  • Label Encoder: Concept + Code
  • One-Hot Encoder: Concept + Python Code
  • Data Preprocessing
Supervised Learning: Regression
  • Simple Linear Regression: Concept
  • Minimizing Cost Function
  • Ordinary Least Square(OLS)
  • Gradient Descent
  • Measuring Regression Model Performance: R^2 (R - Square)
  • Simple Linear Regression: Python Code -1
  • Simple Linear Regression: Python Code -2
  • Assumptions of Linear Regression
  • Multiple Linear Regression: Concept
  • Dummy Variable
  • Multiple Linear Regression: Python - 1
  • Multiple Linear Regression: Python - 2
  • Multiple Linear Regression: Python - 3
  • Polynomial Linear Regression: Concept
  • Polynomial Linear Regression: Python - 1
  • Polynomial Linear Regression: Python - 2
  • Polynomial Linear Regression: Python - 3
  • Polynomial Linear Regression: Python - 4
  • Linear Regressions Comparisons
  • Simple Linear Regression: Quiz
  • Boston Housing Price Prediction
  • Assignment: Predicting Housing Prices (Boston Data Solution): Optional
Supervised Learning: Classification
  • Logistic Regression
  • Confusion Matrix: Measuring Performance of Classification Model
  • Confusion Matrix: Case Study
  • Logistic Regression: Python 1
  • Logistic Regression: Python 2
  • Logistic Regression: Python 3
  • Logistic Regression: Python 4
  • K - Nearest Neighbours Algorithm
  • K - Nearest Neighbours: Python 1
  • K - Nearest Neighbours: Python 2
  • Naive Bayes
  • Naive Bayes: Python Code
  • Pickle File: Saving and Loading ML Models: Python
  • Wine Quality Prediction
  • Assignment 2: Predicting Wine Quality: Optional
  • Classify iris plants into three species
UnSupervised Learning: Clustering
  • K-Means Algorithm
  • Random Initialization Trap
  • Elbow Method: Choosing optimum no of clusters
  • K-Means++ : Python 1
  • K-Means++ : Python 2
  • K-Means++ : Python 3
  • Hierarchical - Agglomerative Algorithm
  • Agglomerative - Dendrogram
  • Agglomerative - Python 1
  • Agglomerative - Python 2
  • Density Based Clustering - DBSCAN
  • DBSCAN - Python 1
  • DBSCAN - Python 2
  • Measuring UnSupervised Clusters Performance
  • Silhouette Index - Python 1
UnSupervised Learning: Association Rule
  • Apriori Algorithm
  • Association Rule Mining
  • Apriori Association: Python 1
  • Apriori Association - Python 2
  • Apriori Association- Python 3
Supervised Learning: Decision Tree and Support Vector Machines
  • Decision Tree Regression - Concept 1
  • Decision Tree Regression - Concept 2
  • Decision Tree Regression - Python 1
  • Decision Tree Regression - Python 2
  • Decision Tree Classification - Concept 1
  • Decision Tree Classification - Concept 2
  • Decision Tree Classification - Python 1
  • Decision Tree Classification - Python 2
  • Support Vector Machines - Concept
  • Support Vector Machines - Python 1