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)

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