- What is Machine Learning?
- Types of Machine Learning
- Supervised Learning
- Quiz 1

- Installing Anaconda
- How to Use Spyder Notebook
- How to use Jupiter Notebook
- Installing Library

- Why AWS?
- Creating EC2 instance
- Connect to EC2 instance
- Installing Packages
- Transferring Files to AWS EC2 instance

- 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

- 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

- 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

- 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

- Apriori Algorithm
- Association Rule Mining
- Apriori Association: Python 1
- Apriori Association - Python 2
- Apriori Association- Python 3

- 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