MACHINE LEARNING OVERVIEW:
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.
Classification of Logistics Regression Cost Function, Simplified Cost Function Regularization (The Problem of Overfitting, Cost Function) Downloading and Installing Anaconda Downloading the IRIS Datasets.
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.