## What you’ll learn

- Learn the use of Python for Data Science and Machine Learning
- Master Machine Learning on Python & R
- Master Machine Learning on Tensorflow
- Learn Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS.
- Learn Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Business Intelligence BI, Regression.
- Learn Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic.
- Learn Numpy, Pandas, Metplotlit, Seaborn.
- Learn Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection.
- Learn Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm.
- Learn Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis

**Machine Learning – Statistics Essentials**

Machine Learning Introduction

Introduction to Machine Learning with Python

Analytics in Machine Learning

Big Data Machine Learning

Emerging Trends Machine Learning

Data Mining

Data Mining Continues

Supervised and Unsupervised

Sampling Method in Machine Learning

Technical Terminology

Error of Observation and Non Observation

Systematic Sampling

Cluster Sampling

Statistics Data Types

Qualitative Data and Visualization

Machine Learning

Relative Frequency Probability

Joint Probability

Conditional Probability

Concept of Independence

Total Probability

Random Variable

Probability Distribution

Cumulative Probability Distribution

Bernoulli Distribution

Gaussian Distribution

Geometric Distribution

Continuous and Normal Distribution

Mathematical Expression and Computation

Transpose of Matrix

Properties of Matrix

Determinants

Error Types

Critical Value Approach

Right and Left Sided Critical Approach

P-Value Approach

P-Value Approach Continues

Hypothesis Testing

Left Tail Test

Two Tail Test

Confidence Interval

Example of Confidence Interval

Normal and Non Normal Distribution

Normality Test

Normality Test Continues

Determining the Transformation

T-Test

T-Test Continue

More on T-Test

Test of Independence

Example of Test of Independence

Goodness of Fit Test

Example of Goodness of Fit Test

Co-Variance

Co-Variance Continues

**Machine Learning with Tensorflow for Beginners**

Introduction to Machine Learning with Tensorflow

Understanding Machine Learning

How do Machines Learns

Uses of Machine Learning

Examples with tensorflow by Google

Setting up the Workstation

Understanding program languages

Understanding and Functions of Jupyter

Learning of Jupyter installation

Understanding what Anaconda cloud is

Installation of Anaconda for Windows

Installation of Anaconda in Linux

Using the Jupyter notebook

Getting started with Anaconda

Determining options for Cloudberry

Introduction to Third Party Libraries

Numpy-Array

Numpy-Array Continue

Arrays

Arrays Continue

Indexing

Indexing Continue

Universal Functions

Introoduction to Pandas

Pandas Series

Pandas Series Continue

Import Randin

Import Randin Continue

Paratmeters

Indexing and Database

Missing Data

Missing Data-Groupby

Missing Data-Groupby Continue

Concat-Merge-Join

Operations

Import-Export

Python Visualisation

Mat Plotting

Multiple Plot Subsections

API Functionality

Title of the Plot

Change Size of Articles

Two Different Crops

Mat Plotting Label

Marker Color

Create a New Dataframe

Change the Style

Index and Value

Seaborn-Statistical Data Visualization

seaborn library

Jointplot

Pairplot

Barplot

Boxplot

Stripplot

Matrix

Matrix Continue

Grid

Grid Continue

Style

Python Libraries Conclusion

Introduction To Conda Envirement

Scikit Learn

Scikit Learn Continue

Datasets

California Dataset

Data Visualization

Datavisualization Continue

Downloading a Test Data

Population Parameter

Processing

Null Values with Median Value

Replace Missing Values

Label Enconder

Import Labelencoder

Custom Transformation

Transformer Custom Transformer

Housing with Custom Colums

Numeric Hosing Data

Liner Regression

Fine Tuning Model

Fine Tuning Model Continue

Quick-Recap

Tensorflow

Tensorflow-Hello-World

Basic Ops

Basic Ops Continue

More on Basic Ops

Eager-Mode

Concept

Linear-Regression

Linear-Model

Matrix Multiplication Function

Practice for a Simple Linear Model

Cost Function

Creative Optimizer

RR Input and Output Value

Logistic-Regression

Global Variabales Initializer

Run Optimizer

Create a Range

Introduction to Neural Networks

Basic-Concepts

Activative Functions

Activative Functions Input to Output

Classification Functions

Tensorflow-Playground

Mnist-Dataset

Mnist-Dataset Continue

More on Mnist-Dataset

**Machine Learning Project #1 – Shipping and Time Estimation**

Introduction to Shipping and pricing

Inventory Status

Defining Data Type

Data for Validation

Finding the Corelation

Density for Numeric Attribute

Method for Train Control

Assigning a Training Set

Mean Absolute Error

Demand Forecasting

Distribution of Attributes

Spending Distribution

Normalization and Discretization

**Machine Learning Project #2 – Supply Chain-Demand Trends Analysis**

Introduction to Supply Chain

G Plot of Heatmap

Checking the Function Argument

Heatmap for Discretized Dataset

Distinguished Methods with Single

Analyzing both the Plots

Defining the Lengths

Using Different Clusters

**Machine Learning Project #3 – Predicting Prices using Regression**

Introduction to Predicting Prices Using Regression

Proximity to Various Conditions

Number of Fire Places

Adding the Test Value

Index to the ID Column

Model on Data Set

Missing Value Imputation

Substituting Features with Value

Imputing a Row using at Command

Replacing Features with Values

Assigning Quantatative Variables

Converting Columns to Cordinal Forms

Evaluating the Garage Finish Colummn

Checking Shape of Data Frame

Spliting Data to Train and Test

Algorithm for Predicting Test Values

**Machine Learning Project #4 – Banking and Credit Frauds**

Introduction to Banking System

Laon Status Grade

Logistic Regression and Logistic Question

Beta Value

Predict Value

Performance Value

Fals Positive Rate

**Machine Learning Project #5 – Fraud Detection in Credit Payments**

Introduction to Fraud Detection in Credit Payments

Installation of Packages

Risk Analytics

Trading Companies and Stocks

DEA with Input or Profit and Loss

Efficiency Profit and Loss

Rank Functions

RHS Constaints

Profit and Loss Report

VRS

CRS Efficiency and Efficiency

**AWS Machine Learning**

Introduction to Amazon Machine Learning (AML)

Lifecycle of AML

Connecting to Data Source in AML

Creating Data Scheme in AML

Invaild Value and Varible Target in AML

ML Models in AML

Manging ML Object in AML

Creating DataSource Handson

Creating DataSource Handson Continues

Example of Data Insight In AML

More on Data Insight In AML

ML Model Example in Data Sources

Creating ML Model Evaluating

Advanced Setting In ML Model

Creating ML Model for Batch Prediction

Batch Prediction Result

Overvies of ML Model Handson

ML objects Handson in ML

**Deep Learning Tutorials**

Introduction to Deep Learning

Structure of Neural Network

Moving Through Neural Network

Types of Activation Functions

Optimizing Back Propagation

Briefing on Tensor Flow

Installation of Tensor Flow

Implementatiion on Neural Package

Implementatiion on Neural Package Continues

Data for Classifier

Implementing with Keras

Values in Data Set

Components in Data Set

Models in Data Set

**Natural Language Processing (NLP) Tutorials**

Intoroduction to NLP

Text Preprocessing

Feature Extraction

NLP Installation

NLP – Demo

Replacing Contractions

Tokenize Dataset

Remove Stopwords

Stemming and Lemmatization

Stemming and Lemmatization Continues

Convert Token No Stopwords

Machine Learning Algorithms

**Bayesian Machine Learning: A/B Testing**

Introduction to Bayesian Machine Learning

Example of Bayesian Machine Learning

Example of Bayesian Machine Learning Continues

MCMC Module of PYMC Implementation

Running the MCMC Module

Multiple Variant Testing Using Hierarchial Model

Example of Multiple Variant Testing

Example of Multiple Variant Testing Continues

**Machine Learning with R**

Introduction to Machine Learning with Python

How do Machine Learn

Steps to Apply Machine Learning

Regression and Classification Problems

Basic Data Manipulation in R

More on Data Manipulation in R

Basic Data Manipulation in R – Practical

Create a Vector

2.7 Problem and Solution

2.10 Problem and Solution

Exponentiation Right to Left

2.13 Avoiding Some Common Mistakes

Simple Linear Regression

Simple Linear Regression Continues

What is Rsquare

Standard Error

General Statistics

General Statistics Continues

Simple Linear Regression and More of Statistics

Open the Studio

What is R Square

What is STD Error

Reject Null Hypothesis

Variance Covariance and Correlation

Root names and Types of Distribution Function

Generating Random Numbers and Combination Function

Probabilities for Discrete Distribution Function

Quantile Function and Poison Distribution

Students T Distribution, Hypothesis and Example

Chai-Square Distribution

Data Visualization

More on Data Visualization

Multiple Linear Regression

Multiple Linear Regression Continues

Regression Variables

Generalized Linear Model

Generalized Least Square

KNN- Various Methods of Distance Measurements

Overview of KNN- (Steps involved)

Data normalization and prediction on Test Data

Improvement of Model Performance and ROC

Decision Tree Classifier

More on Decision Tree Classifier

Pruning of Decision Trees

Decision Tree Remaining

Decision Tree Remaining Continues

General concept of Random Forest

Ada Boosting and Ensemble Learning

Data Visualization and Preparation

Tuning Random Forest Model

Evaluation of Random Forest Model Performance

Introduction to Kmeans Clustering

Kmeans Elbow Point and Dataset

Example of Kmeans Dataset

Creating a Graph for Kmeans Clustering

Creating a Graph for Kmeans Clustering Continues

Aggregation Function of Clustering

Conditional Probability with Bayes Algorithm

Venn Diagram Naive Bayes Classification

Component OF Bayes Theorem using Frequency Table

Naive Bayes Classification Algorithm and Laplace Estimator

Example of Naive Bayes Classification

Example of Naive Bayes Classification Continues

Spam and Ham Messages in Word Cloud

Implementation of Dictionary and Document Term Matrix

Executes the Function Naive Bayes

Support Vector Machine with Black Box Method

Linearly and Non- Linearly Support Vector Machine

Kernal Trick

Gaussian RBF Kernal and OCR with SVMs

Examples of Gaussian RBF Kernal and OCR with SVMs

Summary of Support Vector Machine

Feature Selection Dimension Reduction Technique

Feature Extraction Dimension Reduction Technique

Dimension Reduction Technique Example

Dimension Reduction Technique Example Continues

Introduction Principal Component Analysis

Steps of PCA

Steps of PCA Continues

Eigen Values

Eigen Vectors

Principal Component Analysis using Pr-Comp

Principal Component Analysis using Pr-Comp Continues

C Bind Type in PCA

R Type Model

Black Box Method in Neural Network

Characteristics of a Neural Networks

Network Topology of a Neural Networks

Weight Adjustment and Case Update

Introduction Model Building in R

Installing the Package of Model Building in R

Nodes in Model Building in R

Example of Model Building in R

Time Series Analysis

Pattern in Time Series Data

Time Series Modelling

Moving Average Model

Auto Correlation Function

Inference of ACF and PFCF

Diagnostic Checking

Forecasting Using Stock Price

Stock Price Index

Stock Price Index Continues

Prophet Stock

Run Prophet Stock

Time Series Data Denationalization

Time Series Data Denationalization Continues

Average of Quarter Denationalization

Regression of Denationalization

Gradient Boosting Machines

Errors in Gradient Boosting Machines

What is Error Rate in Gradient Boosting Machines

Optimization Gradient Boosting Machines

Gradient Boosting Trees (GBT)

Dataset Boosting in Gradient

Example of Dataset Boosting in Gradient

Example of Dataset Boosting in Gradient Continues

Market Basket Analysis Association Rules

Market Basket Analysis Association Rules Continues

Market Basket Analysis Interpretation

Implementation of Market Basket Analysis

Example of Market Basket Analysis

Datamining in Market Basket Analysis

Market Basket Analysis Using Rstudio

Market Basket Analysis Using Rstudio Continues

More on Rstudio in Market Analysis

New Development in Machine Learning

Data Scientist in Machine Learnirng

Types of Detection in Machine Learning

Example of New Development in Machine Learning

Example of New Development in Machine Learning Continues

**BIP – Business Intelligence Publisher using Siebel**

Introduction to BIP

User Types

Running Modes

Learning about BIP Add-Ins

BIP_Into_5_BIP_AddIn2 and BIP_Into_6

BIP_Into_7_Customized Reports Overview

BIP_Into_8_Developing Reports Overview

Showing Report Views on Application

Siebel Applets ‚ Business Obejct and Business Components Part 1

Siebel Applets ‚ Business Obejct and Business Components Part 2

IntegrationObjectsANDIntegrationObjectComponents

Siebel Views and View Associations to Reports

Siebel HI-OpenUI framworks for BIP Reports and demo of AddIn

Process_Flow_Overview

Process_Flow_ConnectedMode

Process_Flow_DisconnectedMode

Siebel Report Business Service

**BI – Business Intelligence**

BI Intro,definition

multidimensional db part 1

multidimensional db part 2

multidimensional db part 3

dbms platform

technical non technical infrastructre part 1

technical non technical infrastructre part 2

change control board part 1

change control board part 2

planning deliverables,stage 3

Project Requirement,Data Analysis,Application part 1

Project Requirement,Data Analysis,Application part 2

Project Requirement,Data Analysis,Application part 3

Meta Data

data standardisation,meta data,etl,business analysis part 1

data standardisation,meta data,etl,business analysis part 2

data standardisation,meta data,etl,business analysis part 3

ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 1

ETL Design,Meta DATA ,STAGE 5 CONSTRUCTION DEVELOPMENT RECONCILATION Part 2

ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 1

ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 2

ETL,APPLICATION dEVELOPMENT,DATA gaps,meta data repository,deployment Part 3

database & recovery,release evaluation

post implementation review,toyota case

frame work for BI Part 1

frame work for BI Part 2

frame work for BI Part 3

frame work for BI Part 4

strategic imperitive of BI Part 1

strategic imperitive of BI Part 2

Target System

Data warehouse and ETL

Facebook dataspace management with open source tools

Agile Development Process

Agile Development Process Continues

Challenges on dash board

Building Users Expert Profile

Semantic Technologies

Semantic Tools

BI Algorithm By Example

Benefits of BI

Benefits of BI Continues

Amazon.com and Net Flix

What is Information Governance

Other BI Applications are used to store

Designing and Implementing BI Program

ETL

ETL Continues

Loading

Type 2 Dimension

Loading Fact Tables

Genearl Idea

Conceptual Model

Conceptual Model Continues

On Going Or Future Works

Why Meta Data

Essentials Capabilities

Common Warehouse Metamodels

Data Advantage Group

DBMS Meta Data Tips

For Building The Dataware house(Extraction Team)

Meta Data Essentials For IT

Business Metadata

Business Meta Data (Continues)

Project Planning

Project Planning (Continues)

Deployment Process

Chapter Outline

Break-Even Analysis

Examples Of Break-Even Analysis

Multivirate Analysis

Multivirate Analysis (Continues)

Graphs

Why Meta Data Is Important

System Development

Project Risk Assesment Factors

Managing Project Time

Prototyping Benefits

Incremental Development

Incremental Development(Continues)

What is Cluster Analysis

Types Of Clusters

Cluster Benefits

Kmeans Clustering Method

What Is The Problem With PAM

BIRCH (1996)

Density Rechable And Density Conected

Denclue Technical Issues

The Wave Cluster Algorithm

More On Conceptual Clustering

Clustering in Quest

Why Constraints Based Cluster Analysis

What Is Outlier Discovery

Segmentation In Data Mining

Bottle Neck Of GSP & Spade

Why Deal with Sequential Data

Algorithm Definition

Introduction To Regression Analysis

Regression Model

Regression Model(Continues)

Market Basket Analysis Applications

Market Basket Analysis Applications(Continues)