ADVANCED PYTHON TRAINING

MACHINE LEARNING OVERVIEW:

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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Details Course Content Of Machine Learning Technologies

Machine Learning

  • Module - I
  • Introduction to Machine Learning & It's Uses
  • 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

Machine Learning

  • 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

Machine Learning

  • Module - V
  • Clustering Concepts, MLextend
  • Truncating Dendrogram, k-Means Clustering
  • Working with Artificial Neural Networks
  • Gradient Descent, Stochastic Gradient Descent
  • Gradient Descent, Stochastic Gradient Descent

Machine Learning

  • 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

Machine Learning

  • 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