PG Certificate Program In Machine Learning And Big Data Analytics

Machine Learning

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  • 17-24 LPA

    Average salary in this domain

  • 60000+

    Jobs open every year in this domain

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About this course

Organizations today operate in a world surrounded by data, and data that is understood and analyzed smartly can play a pivotal role in determining the success of many businesses. Data Science, through its various inherent tools and techniques, is successfully adding value to all the business models by using statistics and deep learning to make better, more relevant and timely decisions. Understanding the dynamics of data and knowing how to deal with it has therefore become a critical skill in an organizational context. No wonder that Harvard Business Review has termed Data Scientist as the Sexiest Job of the 21st Century!

The objective of this course is to introduce participants to the intricacies of data science, and techniques of machine learning. This course will expose participants to hands-on experience of popular and in-demand tools in the BDA and ML area. Furthermore, it has been designed with an intention to impart practical problem solving skills to participants which in turn will enhance prospects of career growth.

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Organizations today operate in a world surrounded by data, and data that is understood and analyzed smartly can play a pivotal role in determining the success of many businesses. Data Science, through its various inherent tools and techniques, is successfully adding value to all the business models by using statistics and deep learning to make better, more relevant and timel... Read More

Get a deeper understanding of

  • Mathematical Foundation for Data Analysis
  • Machine Learning Models in Python
  • Data Visualization using Matplotlib
  • Deep Learning using TensorFlow
  • Bigdata with Spark
  • Data Wrangling
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Key skills you’ll learn

  • Probability and statistics
  • Linear Algebra
  • Data Visualization
  • Data Manipulation

Benefits for All Students

Lectures imparted by eminent faculty from IIIT Allahabad and Subject Matter Experts

Knowledge imparted through walkthrough, demonstrations and class exercises to develop hands-on skills

Opportunity to work on a Capstone Project as a part of the program

Program completely oriented towards imparting practical and application oriented knowledge

On successful completion of the course, earn a Certificate of Completion and gain Alumni Status

Opportunity to complete 1 month of professional Internship doing hands-on projects with TransOrg Analytics

Programming languages and tools covered include Python, Scikit Learn, Tensor Flow etc.

Two Campus Immersion modules of 3 days each, at the commencement and culmination of the program, that provides participants with an opportunity to experience the campus, meet the faculty and network with peers

Lectures imparted by eminent faculty from IIIT Allahabad and Subject Matter Experts

Knowledge imparted through walkthrough, demonstrations and class exercises to develop hands-on skills

Opportunity to work on a Capstone Project as a part of the program

Program completely oriented towards imparting practical and application oriented knowledge

On successful completion of the course, earn a Certificate of Completion and gain Alumni Status

Opportunity to complete 1 month of professional Internship doing hands-on projects with TransOrg Analytics

Programming languages and tools covered include Python, Scikit Learn, Tensor Flow etc.

Two Campus Immersion modules of 3 days each, at the commencement and culmination of the program, that provides participants with an opportunity to experience the campus, meet the faculty and network with peers

Are You Eligible?

Are You Eligible

Education

  • For Indian Participants – Graduates (10+2+3) from a recognized university (UGC/AICTE/DEC/AIU/State Government) in any discipline
  • For International Participants – Graduation or equivalent degree from any recognized University or Institution in their respective country

Prerequisites

  • Mathematics or Statistics as a subject in Class XII or Graduation
  • Formal education in, or knowledge/experience of any programming language would be beneficial
  • This program is entirely hands-on and so it is recommended, though not a necessity that students have two devices (laptop/desktop) – one to follow the lecture, and the other for hands-on practice alongside, during the class

Syllabus at a Glance

Data scientists must know how to code - start by learning the fundamentals of one of the most popular programming languages - Python.

Introduction to Python

  • A brief introduction to Python and its installation along with different Integrated Development Environments (IDE) that support Python

Python basics

  • Understanding different variable types and operators in Python

Python data structures

  • Working with different data structures and their usage such as list, dictionary, and tuple

Python programming

  • Understanding concepts of control and looping, conditional statements, and functions in Python programming

Use of standard libraries - Working with Python packages which are specifically tailored for certain functions, such as:

  • NumPy: Also known as Numerical Python mostly used in mathematical operations
  • Pandas: Package to build data frames used for creating features and processing the data
  • Matplotlib and Seaborn: Packages used for data visualization in Python
  • Statsmodel and Scipy: Used for statistical modelling and testing
  • Scikit-Learn: Used for machine learning and advance analytics algorithms

Once you have the core skill of programming covered– dip your feet in the nitty-gritties of working with data, by learning how to wrangle and visualize them.

  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & Pandas
  • Relational Databases and Data Manipulation with SQL
  • Scipy Libraries
  • Loading, Cleaning, Transforming, Merging, and Reshaping Data

It is impossible to use data without knowledge of mathematics. Collect, organize, analyze, interpret, and present data using these concepts of mathematics specially probability, statistics and linear algebra.  

Probability & Statistics

  • Descriptive Statistics & Data Distributions
  • Probability Concepts and Set Theory
  • Probability Mass Functions
  • Probability Distribution Functions
  • Cumulative Distribution Functions
  • Modeling Distributions
  • Inferential Statistics
  • Estimation
  • Hypothesis Testing
  • Implementation of Statistical Concepts in Python  

 

Linear Algebra

  • Vectors
  • Eigen Vectors & Eigen Values
  • Matrix Manipulation
  • Rank

Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.

Building Models Using Below Algorithms

  • Linear and Logistics Regression
  • Support Vector Machines (SVMs)
  • Random Forests
  • XGBoost
  • Decision Tree, Random Forest, K-Nearest Neighbors
  • Partition Based & Hierarchical Clustering
  • Principal Component Analysis
  • Text Analytics

Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib.

  • Interactive Visualizations with Matplotlib
  • Dashboard Development
  • Exploratory Visualization

Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.

  • Basics of Neural Network
  • Linear Algebra
  • Implementation of Neural Network in Vanilla
  • Basics of TensorFlow
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Models
  • Semi-supervised Learning using GAN
  • Seq-to-seq Model
  • Encoder and Decoder

Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.

  • Revision of Data Mining algorithms
  • Introduction to Big Data analytics & Spark
  • RDD’s in Spark, Data Frames & Spark SQL
  • Spark Streaming, MLib & GraphX
  • Time Series Forecasting
  • Predictive analytics

This program includes two on-campus components of 3 days each which will take place at IIIT Allahabad campus. On Campus session 1 is tentatively scheduled towards end of July 2021 and On Campus Session 2 is tentatively scheduled towards end of May 2022. The final confirmed dates for On Campus sessions will be communicated in due course and is subject to Government advisory regarding Covid 19 situation. However, attendance to On Campus Component, when held, will be mandatory for all participants of this course.

  • Focus on real business use cases
  • Enhance theoretical understanding with practical work
  • Learn directly from practicing Data Scientists and subject matter experts (SMEs)
  • Understand the applicability of AI and ML in a particular industry domain

Data scientists must know how to code - start by learning the fundamentals of one of the most popular programming languages - Python.

Introduction to Python

  • A brief introduction to Python and its installation along with different Integrated Development Environments (IDE) that support Python

Python basics

  • Understanding different variable types and operators in Python

Python data structures

  • Working with different data structures and their usage such as list, dictionary, and tuple

Python programming

  • Understanding concepts of control and looping, conditional statements, and functions in Python programming

Use of standard libraries - Working with Python packages which are specifically tailored for certain functions, such as:

  • NumPy: Also known as Numerical Python mostly used in mathematical operations
  • Pandas: Package to build data frames used for creating features and processing the data
  • Matplotlib and Seaborn: Packages used for data visualization in Python
  • Statsmodel and Scipy: Used for statistical modelling and testing
  • Scikit-Learn: Used for machine learning and advance analytics algorithms

Once you have the core skill of programming covered– dip your feet in the nitty-gritties of working with data, by learning how to wrangle and visualize them.

  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & Pandas
  • Relational Databases and Data Manipulation with SQL
  • Scipy Libraries
  • Loading, Cleaning, Transforming, Merging, and Reshaping Data

It is impossible to use data without knowledge of mathematics. Collect, organize, analyze, interpret, and present data using these concepts of mathematics specially probability, statistics and linear algebra.  

Probability & Statistics

  • Descriptive Statistics & Data Distributions
  • Probability Concepts and Set Theory
  • Probability Mass Functions
  • Probability Distribution Functions
  • Cumulative Distribution Functions
  • Modeling Distributions
  • Inferential Statistics
  • Estimation
  • Hypothesis Testing
  • Implementation of Statistical Concepts in Python  

 

Linear Algebra

  • Vectors
  • Eigen Vectors & Eigen Values
  • Matrix Manipulation
  • Rank

Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.

Building Models Using Below Algorithms

  • Linear and Logistics Regression
  • Support Vector Machines (SVMs)
  • Random Forests
  • XGBoost
  • Decision Tree, Random Forest, K-Nearest Neighbors
  • Partition Based & Hierarchical Clustering
  • Principal Component Analysis
  • Text Analytics

Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib.

  • Interactive Visualizations with Matplotlib
  • Dashboard Development
  • Exploratory Visualization

Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.

  • Basics of Neural Network
  • Linear Algebra
  • Implementation of Neural Network in Vanilla
  • Basics of TensorFlow
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Models
  • Semi-supervised Learning using GAN
  • Seq-to-seq Model
  • Encoder and Decoder

Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.

  • Revision of Data Mining algorithms
  • Introduction to Big Data analytics & Spark
  • RDD’s in Spark, Data Frames & Spark SQL
  • Spark Streaming, MLib & GraphX
  • Time Series Forecasting
  • Predictive analytics

This program includes two on-campus components of 3 days each which will take place at IIIT Allahabad campus. On Campus session 1 is tentatively scheduled towards end of July 2021 and On Campus Session 2 is tentatively scheduled towards end of May 2022. The final confirmed dates for On Campus sessions will be communicated in due course and is subject to Government advisory regarding Covid 19 situation. However, attendance to On Campus Component, when held, will be mandatory for all participants of this course.

  • Focus on real business use cases
  • Enhance theoretical understanding with practical work
  • Learn directly from practicing Data Scientists and subject matter experts (SMEs)
  • Understand the applicability of AI and ML in a particular industry domain

Learn from the Best Faculty

About The Institute

CSTCP-IIIT Allahabad

Established in 1999 as a center of excellence in Information Technology and allied areas, Institute was conferred the Deemed University status by Govt. of India in the year 2000.

In 2014 the IIIT Act was passed, under which IIITA and four other Institutes of Information Technology funded by the Ministry of Human Resource Development were classed as Institutes of National Importance. Recently, IIITA Prayagraj has instituted the Centre for Short Term Certification Programmes (CSTCP) with the primary aim of conducting online courses through suitable online platforms provided by various companies.

IIIT-Allahabad was ranked 119 in BRICS nation by the QS World University Rankings of 2019. Among government engineering colleges in India, IIIT-Allahabad ranked 10th by India Today in 2019 and 18 by Outlook India in 2019. It was ranked 82 among engineering colleges by the National Institutional Ranking Framework (NIRF) in 2019. IIIT-A is very famous for its placements and coding culture.

Fee Structure for

Instalment Schedule

Instalment 1
INR 25000 + GST Due by: 22 Jun, 2021
Instalment 1
USD 500 Due by: 22, Jun, 2021
Instalment 2
INR 175000 + GST Due by: 22 Jun, 2021
Instalment 2
USD 3500 Due by: 22, Jun, 2021
OR
Pay for Total Amount
INR 200000 + GST Due by: 22, Jun, 2021
Pay for Total Amount
USD 4000 Due by: 22, Jun, 2021

Financial Aid

To learn more about financing your course, visit the course Financial Resources Hub

Why Should You Join a Program Offered Through Talentedge?

  • 95% Completion Rate
  • 92% Satisfaction Score
  • 78% Referablity
  • Live & Interactive Digital Learning
  • Convenient Schedules to Suit Working Professionals
  • Benefit from Talentedge’s Alumni Network
  • One on one interactions
  • Mobile platform enabled
  • Options for Loan Assistance
  • Live tech support

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