The statistic for Machine Learning is used for searching in the Web. It is very much helpful for placing Ads, Credit scoring. It can be used for stock trading. It is a very vast concept and can be used for many other applications. Learning statistic for Machine learning is helpful in learning algorithms. Machine learning gives detail idea about IN and OUT, Implementing different algorithms in the application of machine learning. You’ll learn the theoretical and practical concepts of Machine learning. It uses computer algorithms to search for any data. Enroll today and attend Statistics and Machine Learning Online Training free demo by real time expert.

Course Objectives

What are the Course Objectives?

After this Statistics and Machine Learning Online Training Course you will able to understand

Complete knowledge of Statistic for Machine learning.

Principal on Statistic.

Get knowledge of Algorithms.

Learn Mathematical and Heuristic concept of Machine learning.

Learn reinforcement and dimensionality reduction.

Who should go for this Course?

Any IT experienced Professional who want to be Machine Learning developer can join Statistics and Machine Learning Online Training.

Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.

Fresh Graduates.

Pre-requisites:

Statistic

Development Concept

Course Curriculum

Statistics

What is Statistics

Descriptive Statistics

Central Tendency Measures

The Story of Average

Dispersion Measures

Data Distributions

Central Limit Theorem

What is Sampling

Why Sampling

Sampling Methods

Inferential Statistics

What is Hypothesis testing

Confidence Level

Degrees of freedom

what is pValue

Chi-Square test

What is ANOVA

Correlation vs Regression

Uses of Correlation & Regression

Machine Learning

Introduction

ML Fundamentals

ML Common Use Cases

Understanding Supervised and Unsupervised Learning Techniques

Clustering

Similarity Metrics

Distance Measure Types: Euclidean, Cosine Measures

Creating predictive models

Understanding K-Means Clustering

Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model

Case study

Implementing Association rule mining

Similarity Metrics

What is Association Rules & its use cases?

What is Recommendation Engine & it’s working?

Recommendation Use-case

Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

How to build Decision trees

What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Decision Tree

Confusion Matrix

Case study

Random Forest Classifier

What is Random Forests

Features of Random Forest

Out of Box Error Estimate and Variable Importance

Case study

Naive Bayes Classifier

Case study

Project Discussion

Problem Statement and Analysis

Various approaches to solve a Data Science Problem

Pros and Cons of different approaches and algorithms.

Linear Regression

Case study

Introduction to Predictive Modeling

Linear Regression Overview

Simple Linear Regression

Multiple Linear Regression

Logistic Regression

Case study

Logistic Regression Overview

Data Partitioning

Univariate Analysis

Bivariate Analysis

Multicollinearity Analysis

Model Building

Model Validation

Model Performance Assessment

Scorecard

Text Mining

Case study

Sentimental Analysis

Case study

Support Vector Machines

Case Study

Introduction to SVMs

SVM History

Vectors Overview

Decision Surfaces

Linear SVMs

The Kernel Trick

Non-Linear SVMs

The Kernel SVM

Deep Learning

Case Study

Deep Learning Overview

The Brain vs Neuron

Introduction to Deep Learning

Introduction to Artificial Neural Networks

The Detailed ANN

The Activation Functions

How do ANNs work & learn

Gradient Descent

Stochastic Gradient Descent

Backpropogation

Convolutional Neural Networks

Convolutional Operation

Relu Layers

What is Pooling vs Flattening

Full Connection

Softmax vs Cross Entropy

What are RNNs – Introduction to RNNs

Recurrent neural networks rnn

LSTMs for beginners – understanding LSTMs

long short term memory neural networks lstm in python

Time Series Analysis

Describe Time Series data

Format your Time Series data

List the different components of Time Series data

Discuss different kind of Time Series scenarios

Choose the model according to the Time series scenario

Implement the model for forecasting

Explain working and implementation of ARIMA model

Illustrate the working and implementation of different ETS models

Forecast the data using the respective model

What is Time Series data?

Time Series variables

Different components of Time Series data

Visualize the data to identify Time Series Components

Implement ARIMA model for forecasting

Exponential smoothing models

Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Implement respective model for forecasting

Visualizing and formatting Time Series data

Plotting decomposed Time Series data plot

Applying ARIMA and ETS model for Time Series forecasting

Forecasting for given Time period

Case Study

Course Objectives

What are the Course Objectives?

After this Statistics and Machine Learning Online Training Course you will able to understand

Complete knowledge of Statistic for Machine learning.

Principal on Statistic.

Get knowledge of Algorithms.

Learn Mathematical and Heuristic concept of Machine learning.

Learn reinforcement and dimensionality reduction.

Who should go for this Course?

Any IT experienced Professional who want to be Machine Learning developer can join Statistics and Machine Learning Online Training.

Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.

Fresh Graduates.

Pre-requisites:

Statistic

Development Concept

Course Curriculum

Statistics

What is Statistics

Descriptive Statistics

Central Tendency Measures

The Story of Average

Dispersion Measures

Data Distributions

Central Limit Theorem

What is Sampling

Why Sampling

Sampling Methods

Inferential Statistics

What is Hypothesis testing

Confidence Level

Degrees of freedom

what is pValue

Chi-Square test

What is ANOVA

Correlation vs Regression

Uses of Correlation & Regression

Machine Learning

Introduction

ML Fundamentals

ML Common Use Cases

Understanding Supervised and Unsupervised Learning Techniques

Clustering

Similarity Metrics

Distance Measure Types: Euclidean, Cosine Measures

Creating predictive models

Understanding K-Means Clustering

Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model

Case study

Implementing Association rule mining

Similarity Metrics

What is Association Rules & its use cases?

What is Recommendation Engine & it’s working?

Recommendation Use-case

Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

How to build Decision trees

What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Decision Tree

Confusion Matrix

Case study

Random Forest Classifier

What is Random Forests

Features of Random Forest

Out of Box Error Estimate and Variable Importance

Case study

Naive Bayes Classifier

Case study

Project Discussion

Problem Statement and Analysis

Various approaches to solve a Data Science Problem

Pros and Cons of different approaches and algorithms.

Linear Regression

Case study

Introduction to Predictive Modeling

Linear Regression Overview

Simple Linear Regression

Multiple Linear Regression

Logistic Regression

Case study

Logistic Regression Overview

Data Partitioning

Univariate Analysis

Bivariate Analysis

Multicollinearity Analysis

Model Building

Model Validation

Model Performance Assessment

Scorecard

Text Mining

Case study

Sentimental Analysis

Case study

Support Vector Machines

Case Study

Introduction to SVMs

SVM History

Vectors Overview

Decision Surfaces

Linear SVMs

The Kernel Trick

Non-Linear SVMs

The Kernel SVM

Deep Learning

Case Study

Deep Learning Overview

The Brain vs Neuron

Introduction to Deep Learning

Introduction to Artificial Neural Networks

The Detailed ANN

The Activation Functions

How do ANNs work & learn

Gradient Descent

Stochastic Gradient Descent

Backpropogation

Convolutional Neural Networks

Convolutional Operation

Relu Layers

What is Pooling vs Flattening

Full Connection

Softmax vs Cross Entropy

What are RNNs – Introduction to RNNs

Recurrent neural networks rnn

LSTMs for beginners – understanding LSTMs

long short term memory neural networks lstm in python

Time Series Analysis

Describe Time Series data

Format your Time Series data

List the different components of Time Series data

Discuss different kind of Time Series scenarios

Choose the model according to the Time series scenario

Implement the model for forecasting

Explain working and implementation of ARIMA model

Illustrate the working and implementation of different ETS models

Forecast the data using the respective model

What is Time Series data?

Time Series variables

Different components of Time Series data

Visualize the data to identify Time Series Components

Implement ARIMA model for forecasting

Exponential smoothing models

Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Implement respective model for forecasting

Visualizing and formatting Time Series data

Plotting decomposed Time Series data plot

Applying ARIMA and ETS model for Time Series forecasting

Forecasting for given Time period

Case Study