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 Steps of 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 KNN Implementation

o o Understanding Hyper Parameters

Performance Metrics

o o Math Behind KNN

o o KNN Implementation

o o Understanding Hyper Parameters

Regression

o o Regression Analysis

o o Simple Linear Regression

o o Multiple Linear Regression

o o Polynomial Regression

o o Boston House Price Prediction

o o Cost Functions and Loss Functions

o o Mean Squared Error (MSE)

o o Root Mean Square Error

o o Least Square Error

o o Regularization

Logistic Regression for Classification

o o Classification Algorithm

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 Implementation

o o SVM Kernel

K-Nearest Neighbor(KNN)

o o Theory of K-Nearest Neighbor(KNN)

o o Implementation of KNN

Naïve Bayes Classifier

o o Theory of Naïve Bayes Classifier

o o Implementation of Naive Bayes Algorithm

Decision Tree Classification

o o Theory of Decision Tree

o o Implementation of Decision Tree

Random Forest

o o Theory of 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 Overview of Clustering

o o K-Means Clustering

o o Mean-Shift Clustering

o o Hierarchical Clustering

o o Elbow Method

o o Silhouette Coefficient

o o Dendogram Method

Text Analysis

o o Text Processing

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