Post Graduate Program in

Machine Learning and Artificial Intelligence

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About the course

Key Highlights

  • Designed for Working Professionals
  • 260+ Hours of Recorded Video Tutorials
  • 25+ In-class & Capstone projects
  • Live Classes- 5 days in a week & one day doubt clearing session
  • Career assistance videos
  • Placement Assistance (Job Opportunities Portal, Hiring Drives, Resume Building & more)
  • EMI Option Available
  • Post Graduate Program Certification from IIBM Institute along with Internship Certificate
  • Course Duration: 6 Months
  • Eligibility: Any Bachelors degree with 50% or equivalent at graduation, No minimum work experience required

CURRICULUM HIGHLIGHTS

Business Analytics
Data Analytics across Domains What is Analytics? Types of Analytics AI vs ML vs DL vs DS
Introduction to statistics and Central Limit Theorem Measures of Central Tendancies and Measures of Spread Descriptive Statistics with Real Time Examples Measuring Scales Inferential Statistics with Real Time Examples
Introduction to statistics and Central Limit Theorem Measures of Central Tendancies and Measures of Spread Descriptive Statistics with Real Time Examples Measuring Scales Inferential Statistics with Real Time Examples

Python Intro,IDE and Python Packages
Python Programming
Python Data Types – Dictionary, List and Set
Numpy Packages – Array Handling and Manupulation
Pandas Packages – Dataframe and Loading Excel, CSV File
Matplotlib Packages – Line graph and Visualisation
Histogram, Scatter Diagram, Box Plot and Bar Graph
Area Chart, Dual Axis, Array reshaping, reverse matrix analysis
Python – Operators and String Manupulation Control Structures(IF,IF-ELSE,IF-ELIF-ELSE,WHILE & FOR LOOP)
Python – Data Preparation Process
Python – Functions WITH and WITHOUT arguments
Python – File Processing and Data Collection Methods
Python – Time Series Analysis and Forcasting
Python – Simple Predictive Analysis

Data Science with Python
Data Science Application across Multiple Domain and Business Function
Data Science Project LifeCycle
Multiple Predictive Model using Python
Python – Simple and Multiple Predictive Model in Practical
Python Correlation Analysis
Python Classication Model Buildin
Data Science – Experimental Design Analysis
Classication Technique – Discriminant Analyssi
Data Science – Association Rule – Apriori Algorithm
Data Science – Building Recommendation System – (Market Basket Analysis) Data Architecture Design, Data Warehousing and it’s Schema Design Image Processing and Image Extraction Image Processing and Object Recognition Summarisation of Data Science Algorithm (Data Science Process)

Machine Learning: Supervised Learning- Algorithm

Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning

Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building

Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classication, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling

ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA

Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis

Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes

Distance as Classier, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classication

Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear

Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling

Machine Learning: Unsupervised Learning- Algorithm

Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA

Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method

Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule

Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation

Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models

Deep Leaning

Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function

Tensorflow Introduction

Building Artificial Neural Networks (ANN)
Deep Learning-ANN-classification

Computer-Vision-opencv-part1-overview
Computer-Vision-opencv-part2-face_detection
intro to CNN

Introduction to RNN & Sequence prediction using RNN

Introduction to LSTM,
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data

Natural Language Processing

Basics of NLP
Removing Stop Words
Stemming & lemmatization
Parts of speech tagging
TFIDF vectorizer
Senmiment Analysis

Text Classification with Linear Models
Language Modelling with Probabilistic Graphical Models and Neural Networks
Word Embeddings and Topic Models
Machine Translation and Sequence-To-Sequence Models

Reinforcement Learning

Model-Based Reinforcement Learning (Dynamic Programming)
Model-Free Reinforcement Learning (SARSA, Monte Carlo, Q-Learning)
Approximate and Deep Reinforcement Learning (Deep Q-Learning)
Policy Gradient Reinforcement Learning
Advanced Topics on Exploration and Planning

Big Data Fundamentals and Platforms for Big Data
What is BigData
Characterstics of BigData
Problems with BigData
Handling BigData

 

Linux Commands
HDFS Commands
SQOOP ARCH and HANDSON:
How Import data from Target RDBMS TO HDFS.
 USecase1: With Primary Key and Without Primary Key
useCase2: Boundary Query Without columns and With Columns
UseCase3: Incremenatl Load
Usecase4: How to Import all tables at a time
Usecase5: How to Import all Tables with Exclude Tables
UseCase6: How to Create Sqoop Job
UseCase7: How to Use $Conditions in Sqoop
UseCase8: How to Import data from RDBMS to HIVE TABLE
Usecase9: How to Process Semi Structured data using Sqoop
Usecase10: Sqoop Export from HDFS to RDBMS
HIVE ARCH AND HANDSON:
Different Types OF Tables In Hive
PARTITIONING
Different Types Of Partitioning
Bucketing
How to Perform Both Partitioning and Bucketing using one table
Joins(Reducer Side Joins and MapSide Joins)
How to Semi Structured Data using Hive
Different File Format In Hive
How to perform Updates and Deletes in Hive
Hive Complex Types
Hive UDf
HBASE ARCH AND HANDSON:
Differnce Between Hive,SQL and HBASE
How to create tables,insert,update and delete
How to import data from rdbms to HBASE using Sqoop
How to Load CSV DATA INTO HBASE TABLE
HIVE to HBASE INTEGRATION
PIG AND MAPREDUCE
SCALA:
What Is Scala
Differnce between JAVA and SCALA
SCala Variables
For,While and Do while Loop
Condiotional Statements
String,String Methods,String Interpolation
Functions
Higher Order Functionss
Anonymous Functions
Closure Function
Currying Function
Collections(Array,set,tuple,map and list)
File Handling
Exception Handling
Traits

 

Spark vs Map Reduce
Architecture of Spark
Spark Shell introduction
Creating Spark Context
Spark Project with Maven in Eclipse
Cache and Persist in Spark
File Operations in Spark

RDD:

What is RDD
Transformations and Actions
Loading data through RDD
key-value pair RDD
Pair RDD oeprations
Running spark application with Spark-shell
Deploying Application With Spark-Submit

Spark-SQL:

introduction to Spark SQL
Hive vs SparkSQL
Processing different fileformats using Spark SQL
DataFrames
DAG
Lineage Graph
Cluster types
Optimizers
Structured Streaming
RDDs to Relations
Spark Streaming:

introduction to Spark Streaming
Architecture of spark Streaming
SparkStreaming vs Flume
introduction to Kafka
Kafka Architecture
Spark Streaming integration with Kafka Overview
Real Time Examples

 

Data Visulaztaion Using Tableau

Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau

Data Science Using "R"

What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.

Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions

Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.

R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data

Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages

Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.

Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression

Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example

Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering

Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis

Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes

K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN

What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree

Ensample of Decision Tree.

Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM

RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics

PROGRAMMING LANGUAGES AND TOOLS

Capstone Projects

Capstone Projects

Financial Analytics

Unsupervised Machine Learning – Merger and Acquisitions Analytics

Banking Analytics

Bank Loan Modeling – Automation of loan eligibility process – Dream Housing Finance Company

Gaming Analytics

Prediction of English Premier League (EPL) Championship

Supply Chain Analytics

Zomato Delivery Performance Analysis

HR Analytics

Employee Attrition Rate Analysis

Banking Analytics

Team Deposit Plan – Machine Learning Classification – Portuguese Bankinh Institution

Retail Analytics

Predicting house prices for using supervised Machine Learning

Real Estate Analytics

Predictive Analytics with model simulationm – Ames Housing Authority.

HR Analytics

Employee Termination Analysis

Customer Analytics

Principal Component Analysis – Dimension Reduction – LKP Share & Securities.

Machine Learning

Telecom Chrum Case Study Using Sklearn

Artificial NN

Handwritten Digit Classification Using ANN

Machine Learning

Recommendation Engine

Natural Language Processing

Sentiment Analyser

Natural Language Processing

Building Chatbot

Artificial Intelligence

SMS Spam,Classifier

Artificial Intelligence

Twitter Sentiment Analyser

In-Class capston Projects

Financial Market Prediction

Analysis the Real-time Stock market using Regression

Diabetic Diagnostic Prediction

Predict the Medical condition of Person

Flower Species Classification

Iris Flower classification is done using sepal and petal

Titanic Survival

Will person survive on titanic ship .

Cancer Detection

Will classify the person detect with cancer or not Customer Segmentation : Customer will divided into segments and behavior will analyze

Loan Prediction

Person will be loan defaulters in future of not .

MNIST Digit Classification

kids Handwritten digits will be classified

Wine Quality Test

test the wine quality and classifiy it.

CERTIFICATION

WILL I GET CERTIFIED?

On completion of the Post Graduate Program in Machine Learning and Artificial Intelligence, aspirants will receive an Industry-endorsed Certificate along with Internship Certificate.

MENTORSHIP

Our Industry mentor team will guide you with:

– Provide unparalled 1:1 support and guidance
– Help execute in-class assignments and Projects
– Discuss & identify learning gaps and other solutions such as refresher sessions and one-on-one project feedback
-Set learning goals
-Discuss your progress status with trainers and other industry mentors on a regular basis to ensure consistent advancement

Dinesh
Babu R

Qualification: P.hd in Data Analysis, MBA (Finance & Operation), B.Tech

Abhishek Srivastava

Qualification: M.Tech (CS), MCA

Suryanarayana Murthy

Qualification: MCA, B.Sc (Electronics)

Program Fees

Fees

INR 45,000

INR 35,000

*18% GST Extra

Financing Option:

” IIBM Institute of Business Management” provides education loan through a Lendbox.

For more details contact to loansolutions@iibminternships.com

Our Faculty