 Build Model of Training Data Set

 Build Model of Training Data Set

 Predict using Testing Data Set

 Validate the Model Performance

4
 Best Fit Line and Linear Regression

 Model Predictions

 Model Accuracy

 Graphical Plotting


Logistic Regression

 Assumptions

 Reason for the Logit Transform

 Logit Transformation

 Hypothesis

 Variable and Model Significance

 Maximum Likelihood Concept

 Log Odds and Interpretation

 Null Vs Residual Deviance

 Chi-Square Test

 ROC Curve

 Model Specification
 Case for Prediction Probe

 Model Parameter Significance Evaluation

 Drawing the ROC Curve

 Optimization of threshold value

 Estimating the Classification Model Hit Ratio

 Isolating the Classifier for Optimum Results

 Model Accuracy

 Model Prediction


Practical on Machine Linear & Logistic Regression


Support Vector Machine

 Concept and Working Principle

 Mathematical Modelling

 Optimization Function Formation

 The Kernel Method and Nonlinear Hyperplanes

 Optimal separating hyperplane

5
 Drawing Margins

 Use Cases & Programming SVM using Python

 Anomaly Detection with SVM

 Use Cases & Programming using Python

 Case study of KNN Vs SVM

 Applying KNN & SVM for Supervised Problems of Classification &
Unsupervised problems like Clustering.

RANDOM FOREST & Decision Tree Algorithm

 Concept and Working Principle

 Mathematical Modelling

 Optimization Function Formation

 Analysis of Classification Problem case

 Math: Role of Gini Index in Decision Trees

 Analysis of Regression Problem case

 Use Cases & Programming using Python

 Classification with Random Forest

 Pros & Cons

 Project: Credit card fraud analysis

 Use cases & examples

Gradient descent variants (Theory)

 Batch gradient descent

 Stochastic gradient descent

 Mini-batch gradient descent

 Entropy Function

Challenges

Gradient descent optimization algorithms

 Adagrad

 Adadelta

 RMSprop

 Adam

 Visualization of algorithms

 Which optimizer to choose

6


Artificial Neural Networks

Introduction to TensorFlow & Keras

 Introduction Tensorflow

 Tensorflow

 MNIST

 The Programming Model

 Data Model, Tensor Board

 Introducing Feed Forward Neural Nets

 Softmax Classifier & ReLU Classifier

 Deep Learning Applications

 Working with Keras

 Building Neural Network with keras

 Examples and use cases


Artificial Neural Networks with Case Study

 Neurons, ANN & Working

 Single Layer Perceptron Model

 Multilayer Neural Network

 Feed Forward Neural Network

 Cost Function Formation

 Entropy
 Cost Function Optimization

 Applying Gradient Descent Algorithm

 Stocha

 Backpropagation Algorithm & Mathematical Modelling

 Programming Flow for backpropagation algorithm

 Use Cases of ANN

 Programming SLNN using Python

 Programming MLNN using Python

 XOR Logic using MLNN & Backpropagation

 Score Predictor

7
Unsupervised Learning Clustering

 Clustering Introduction

 K-Means Clustering

 Handling K-Means Clustering

 Maths behind KMeans Clustering

 K Means from scratch

 Mean shift Introduction

 Dynamically weight

 Project: Intruder Detection

 Classification problems & best predictive out of all

 Case study for various examples

Flask Web Development
 Introduction
 HTML Basic
 Bootstrap
 Flask Templating
 CRUD
 Project Development
 Project Deployment on Heroku Cloud