Course Details

Machine Learning

 
Course Modules
 
Module - I
 
Introduction to Machine Learning & It's Uses
Linear Regression and Linear Algebra
Linear Regression and Linear Algebra
Model Representation, Cost Function, Gradient Descent
Matrices and Vectors, Addition and Scalar Multiplication, Inverse and Transpose
Gradient Descent for Multiple Variables, Features of Polynomial
Regression, Normal Equation
 
Module -II
 
Classification of Logistics Regression
Cost Function, Simplified Cost Function
Regularization (The Problem of Overfitting, Cost Function)
Downloading and Installing Anaconda
Downloading the IRIS Datasets
 
Module -III

Introduction to Support Vector Machine
Linear SVM Classification, Polynomial Kernel
Support Vector Regression
Decision Tree, Visualizing a Decision Trees
Decision Tree Regression, Overfitting and Grid Search

 

Module - IV 
 
Introduction to Ensemble Machine Learning
AdaBoost, Gradient Boosting Machine, XGBoost
Introduction to kNN and its Concepts
Introduction to Cancer Detection Project
Dimensionality Reduction Concept
LDA & Comparison between LDA and PCA
 
Module - V
 
Clustering Concepts, MLextend
Truncating Dendrogram, k-Means Clustering
Working with Artificial Neural Networks
Gradient Descent, Stochastic Gradient Descent
Live Projects

 

 

 

 

 

 

 

 

 

 

 

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