## Essentials for Machine Learning

- Mean, Median, Mode, and Range for Machine Learning
- Standard deviation and Variance for Machine Learning

## Machine Learning Course (Videos)

- Introduction to Machine Learning (Lecture 1)
- Review of Matrices and Vectors (Lecture 2)
- Linear, quadratic, cubic equations and derivatives – A review (Lecture 3)
- Introduction to Linear Regression (Lecture 4)
- Simple Linear Regression (Lecture 5)
- Gradient Descent Algorithm (Lecture 6)
- Multiple Linear Regression (Lecture 7)
- Polynomial Regression & Normal Equation Method (Lecture 8)
- Classification and Logistic Regression (Lecture 9)
- Logistic Regression cont… (Lecture 10)
- Overfitting and regularization (Lecture 11)
- KNN Algorithm for Classification (Lecture 12)
- Introduction to Artificial Neural Network (Lecture 13)
- Perceptron Learning Rule (Lecture 14)
- Gradient Descent Algorithm for ANN (Lecture 15)
- Backpropagation Algorithm for Multilayered ANN (Lecture 16)
- Issues in Artificial Neural Network (Lecture 17)
- Introduction to Recurrent Neural Network (Lecture 18)
- Applying Machine Learning (Lecture 19)
- Performance Metrics and System Design (Lecture 20)
- Review of Probability (Lecture 21)
- Naive Bayes Classifier (Lecture 22)
- K-Means Algorithm for Clustering (Lecture 23)
- Hierarchical Clustering (Lecture 24)

## Artificial Neural Network

##### Video Lectures

- Derivation of Sigmoid/Logistic Function
- Derivation of Error Function w.r.t Connection Weights (Quick Review)
- Derivation of Error Function w.r.t Connection Weights (Detailed Discussion)
- Matrix and Vector-based Implementation of ANN
- MATLAB implementation of the Backpropagation Algorithm for MLP to solve XOR problem