Introduction to Machine Learning
o o Introduction to Machine Learning
o o Traditional v/s Machine Learning
o o Real Life Example based on Machine Learning
o o Data Preprocessing Revised
o o Terminology Related to Machine Learning
Supervised Learning
o o Classification
o o Regression
Unsupervised Learning
o o Clustering
KNN Classification
o o Math Behind KNN
o o Understanding Hyper Parameters
Performance Metrics
o o Math Behind KNN
o o Understanding Hyper Parameters
Regression
o o Multiple Linear Regression
o o Boston House Price Prediction
o o Cost Functions and Loss Functions
o o Least Square Error
o o Regularization
Logistic Regression for Classification
o o Theory of Logistic Regression
o o Binary and Multiclass Classification
Support Vector Machines (SVM)
o o Theory of Support Vector Machine
o o SVM Kernel
K-Nearest Neighbor(KNN)
o o Theory of K-Nearest Neighbor(KNN)
Naïve Bayes Classifier
o o Theory of Naïve Bayes Classifier
o o Implementation of Naive Bayes Algorithm
Decision Tree Classification
o o Implementation of Decision Tree
Random Forest
o o Implementation of Random Forest
Model Selection Techniques
o o Cross Validation
o o Grid and Random Search for Hyper Parameter Tuning
Clustering
o o Elbow Method
o o Dendogram Method
Text Analysis
o o Text Processing