03rd April

Batch Starts

11 months

Duration

Online

Format

29+

Projects

E&ICT, IIT Roorkee

Certificate

13,500+

Learners

E&ICT

E&ICT Academy, IIT Roorkee

An initiative of Ministry of Electronics and Information Technology (MeitY) Govt. of India

About the Course

This Data Science and AI Certification Program is an online course. This course covers some of the most trending and latest technologies in the market like Tensorflow 2.0, Generative Adversarial Networks (GANs) etc. The cutting edge content provided through this course will help you launch a career in the field of Data Science

Additionally, this course comes with our cloud lab access to gain the much needed hands-on experience to solve the real-world problems.

Upon successfully completing the course, you will get the certificate from E&ICT Academy, IIT Roorkee which you can use for progressing in your career and finding better opportunities.

Program Highlights

  • Certificate of Completion by E&ICT Academy, IIT Roorkee

  • 11 Months of Blended Learning

  • Work on about 29+ projects to get hands-on experience

  • Timely Doubt Resolution

  • Best In Class Curriculum

  • Cloud Lab Access

Batch Starts on 03rd April, 2021

Certificate

What is the certificate like?

  • Why E&ICT, IIT Roorkee?

    Electronics & ICT Academy IIT Roorkee (E&ICT IITR), provides certification courses with emphasis on hands-on learning in basic/advanced topics and emerging technologies in the Electronics and ICT domain. It is sponsored by Ministry of Electronics and Information Technology, Govt. of India. We conduct certification courses/short courses/FDPs in the emerging areas to enrich & upgrade subject knowledge and technical skills benefiting students, working professionals, Govt. employees and Faculty members. The trained beneficiaries are expected to create a cascading effect, transforming the competencies and standards in the parent institutes/organizations. E&ICT courses are at par with QIP for recognition/credits. So far the E&ICT Academy, IIT Roorkee has conducted 150+ courses and trained over 10,000 beneficiaries.

  • Why Cloudxlab?

    CloudxLab (CxL) has been a pioneer in the edtech space for the past few years. Founded in 2015 by Sandeep Giri, an alumnus of IIT Roorkee, CxL has successfully transformed 1,000's of students' careers by offering world-class certification courses in big data, machine learning and artificial intelligence.

    Some of the unique features of CxL are an exclusive gamified learning environment through the lab (read as CloudxLab), highest rated faculty, excellent student support and more.

Hands-on Learning

hands-on lab
  • Gamified Learning Platform


  • Auto-assessment Tests


  • No Installation Required

Mentors / Faculty

Instructor Raksha Sharma

Raksha Sharma

Faculty CSE Dept

IIT Roorkee

Instructor Gaurav Dixit

Gaurav Dixit

Faculty DoMS Dept

IIT Roorkee

Instructor Sanjeev Manhas

Sanjeev Manhas

Faculty ECE Dept

IIT Roorkee

Mentor Venkat Karun

Venkat Karun

Staff Software Engineer

Google

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Curriculum

11+
Months of Blended Training
330
Days of Lab Access
29+
Projects
13K+
Learners
Foundation
1. Linux for Data Science
2. Getting Started with Git
3. Python Foundations
4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)
5. Getting Started with SQL
6. Statistics Foundations

Machine Learning & Deep Learning

1. Introduction to Machine Learning and Deep Learning
In this topic, we will cover concepts like different types of Machine Learning algorithms (Supervised, Unsupervised, Reinforcement) and challenges in Machine Learning. We will see examples of solving the problems using the traditional approach and why Machine Learning algorithms give far better accuracy than the traditional approach. This topic will give you a brief introduction to both Machine Learning and Deep Learning world.
2. Data Preprocessing, Regression - Build end-to-end Machine Learning Project
We will start the course by learning concepts in Machine Learning. In this topic, we will build a machine learning model to predict housing pricing in California. By the end of this project, you will understand how to build machine learning pipelines to build a model. We will also cover concepts like data cleaning, preparing data for machine learning algorithms, exploring many different models, short-list the best one and fine-tuning the selected model
3. Classification
In this topic, we will train a model on the MNIST dataset to recognize handwritten digits. We will also learn various performance measures in classification like Confusion Matrix, Precision and Recall, and ROC Curve.
4. Machine Learning Algorithms
In this topic, we will learn various Machine Learning algorithms and concepts like Unsupervised Learning, Ensemble Learning, and Dimensionality Reduction
5. Introduction to Artifical Neural Networks with Keras
We will start the Deep Learning course with Artificial Neural Networks. We will learn about biological neurons, multilayer perceptrons, and back-propagation. We will implement a multilayer perceptron using Keras and visualize the runs and graphs using Tensorboard
6. Training Deep Neural Networks
In this topic, we will learn various challenges deep neural networks face while training like vanishing and exploding gradients. We will learn various techniques to solve these problems like reusing pre-trained layers, using faster optimizers and avoiding overfitting by regularization.
7. Custom Models and Training with TensorFlow
In this topic, we will dive deeper into TensorFlow and its lower level Python API. These lower-level Python APIs are useful when we need extra control like writing custom loss function, layers and many more.
8. Loading and Preprocessing Data with TensorFlow
Deep Learning systems are usually trained on very large datasets that may not fit in the RAM. In this topic, we will learn TensorFlow's Data API which helps in ingesting dataset and preprocessing it efficiently.
9. Deep Computer Vision using Convolutional Neural Network
In this topic, we will learn how Convolutional Neural Networks - CNNs achieve superhuman performance on complex visual tasks. Today CNNs power image search services, self-driving cars, automatic video classification systems and more. We will learn CNNs basic building blocks and how to implement them using TensorFlow and Keras
10. Processing Sequences Using RNNs and CNNs
Predicting the future is something we do all the time like predicting stock prices. In this topic, we will learn how Recurrent Neural Networks - RNN predict the future, the problem they face like limited short-term memory and solutions to these problems - LSTM (Long Short-Term Memory) and GRU cells
11. Natural Language Processing Concepts and RNNs
Using Natural Language Processing we build systems that can read and write natural language. In this topic, we will learn different NLP techniques and generate Shakespearean text using a Character RNN.
12. Representation Learning & Generative Learning Using autoencoders and GANs
Autoencoders are artificial neural networks capable of learning dense representations of input data without any supervision. For example, we could train an autoencoder on pictures of faces and it can then generate new faces. In this topic, we will learn different types of autoencoders and generative models.
13. Reinforcement Learning
Reinforcement Learning is one of the most exciting fields of Machine Learning. Using Reinforcement Learning AlphaGo(system) defeated the world champion at the game of Go. Reinforcement Learning is an area of Machine Learning aimed at creating agents capable of taking actions in an environment in a way that maximizes rewards over time. In this topic, we will learn various concepts in Reinforcement Learning and experiment with OpenAI Gym.

Course on Big Data with Hadoop

1. Introduction
1. Introduction
2. Distributed systems
3. Big Data Use Cases
4. Various Solutions
5. Overview of Hadoop Ecosystem
6. Spark Ecosystem Walkthrough
2. Foundation & Environment
1. Understanding the CloudxLab
2. Getting Started - Hands on
3. Hadoop & Spark Hands-on
4. Understanding Regular Expressions
5. Setting up VM
3. Zookeeper
1. ZooKeeper - Race Condition
2. ZooKeeper - Deadlock
3. How does election happen - Paxos Algorithm?
4. Use cases
5. When not to use
4. HDFS
1. Why HDFS?
2. NameNode & DataNodes
3. Advance HDFS Concepts (HA, Federation)
4. Hands-on with HDFS (Upload, Download, SetRep)
5. Data Locality (Rack Awareness)
5. YARN
1. Why YARN?
2. Evolution from MapReduce 1.0
3. Resource Management: YARN Architecture
4. Advance Concepts - Speculative Execution
6. MapReduce Basics
1. Understanding Sorting
2. MapReduce - Overview
3. Word Frequency Problem - Without MR
4. Only Mapper - Image Resizing
5. Temperature Problem
6. Multiple Reducer
7. Java MapReduce
7. MapReduce Advanced
1. Writing MapReduce Code Using Java
2. Apache Ant
3. Concept - Associative & Commutative
4. Combiner
5. Hadoop Streaming
6. Adv. Problem Solving - Anagrams
7. Adv. Problem Solving - Same DNA
8. Adv. Problem Solving - Similar DNA
9. Joins - Voting
10. Limitations of MapReduce
8. Analyzing Data with Pig
1. Pig - Introduction
2. Pig - Modes
3. Example - NYSE Stock Exchange
4. Concept - Lazy Evaluation
9. Processing Data with Hive
1. Hive - Introduction
2. Hive - Data Types
3. Loading Data in Hive (Tables)
4. Movielens Data Processing
5. Connecting Tableau and HiveServer 2
6. Connecting Microsoft Excel and HiveServer 2
7. Project: Sentiment Analyses of Twitter Data
8. Advanced - Partition Tables
9. Understanding HCatalog & Impal
10. NoSQL and HBase
1. NoSQL - Scaling Out / Up
2. ACID Properties and RDBMS Story
3. CAP Theorem
4. HBase Architecture - Region Servers etc
5. Hbase Data Model - Column Family Orientedness
6. Getting Started - Create table, Adding Data
7. Adv Example - Google Links Storage
8. Concept - Bloom Filter
9. Comparison of NOSQL Databases
11. Importing Data with Sqoop and Flume, Oozie
1. Sqoop - Introduction
2. Sqoop Import - MySQL to HDFS
3. Exporting to MySQL from HDFS
4. Concept - Unbounding Dataset Processing or Stream Processing
5. Flume Overview: Agents - Source, Sink, Channel
6. Data from Local network service into HDFS
7. Example - Extracting Twitter Data
8. Example - Creating workflow with Oozier

Course on Big Data with Spark

1. Introduction
1. Apache Spark ecosystem walkthrough
2. Spark Introduction - Why Spark?
2. Scala Basics
1. Introduction, Access Scala on CloudxLab
2. Variables and Methods
3. Interactive, Compilation, SBT
4. Types, Variables & Values
5. Functions
6. Collections
7. Classes
8. Parameters
3. Spark Basics
1. Apache Spark ecosystem
2. Why Spark?
3. Using the Spark Shell on CloudxLab
4. Example 1 - Performing Word Count
5. Understanding Spark Cluster Modes on YARN
6. RDDs (Resilient Distributed Datasets)
7. General RDD Operations: Transformations & Actions
8. RDD lineage
9. RDD Persistence Overview
10. Distributed Persistence
4. Writing and Deploying Spark Applications
1. Creating the SparkContext
2. Building a Spark Application (Scala, Java, Python)
3. The Spark Application Web UI
4. Configuring Spark Properties
5. Running Spark on Cluster
6. RDD Partitions
7. Executing Parallel Operations
8. Stages and Tasks
5. Common Patterns in Spark Data Processing
1. Common Spark Use Cases
1. Example 1 - Data Cleaning (Movielens)
1. Example 2 - Understanding Spark Streaming
2. Understanding Kafka
3. Example 3 - Spark Streaming from Kafka
4. Iterative Algorithms in Spark
5. Project: Real-time analytics of orders in an e-commerce company
6. Data Formats & Management
1. XML
2. AVRO
3. How to store many small files - SequenceFile?
4. Parquet
5. Protocol Buffers
6. Comparing Compressions
7. Understanding Row Oriented and Column Oriented Formats - RCFile?
7. DataFrames and Spark SQL
1. Spark SQL - Introduction
2. Spark SQL - Dataframe Introduction
3. Transforming and Querying DataFrames
4. Saving DataFrames
5. DataFrames and RDDs
6. Comparing Spark SQL, Impala, and Hive-on-Spark
8. Machine Learning with Spark
1. Machine Learning Introduction
2. Applications Of Machine Learning
3. MlLib Example: k-means
4. SparkR Example

Projects

Apply Now

Application Process

  1. Step 1. Submit the application form and SOP(Statement of Purpose)
    Register by filling the application form
  2. Step 2. Reviewing the application
    The admission team will review the application and respond with the application status in 48 hours
  3. Step 3. Join The Program
    Confirmation of seat is subject to the payment

No Cost EMI at

164/Month

Or Program Fee 1799

  • 11 Months of Blended Learning
  • 330 Days of Online Lab Access
  • 24*7 Support
  • Batch Starts on 03rd April, 2021
  • Certificate from E&ICT Academy, IIT Roorkee
Apply Now »
  • Please note that there is an additional 5% off for one-time payment
  • Testimonials

    Frequently Asked Questions

    What is the application process for the program and what are the timelines?

    Applications have already started for this course. You can start the application process by submitting the application form and eligibility quiz. Then our admission committee will take a call on approval and revert back. After making the payment you will get access to the self-paced content, then live classes of Batch will start from 28 March 2021.

    How does the EMI Payment model work?

    The EMI payment starts from April 2021. The monthly EMI payments should be cleared before the 5th of every month. Failure to make the payments will lead to the removal of course access from your account

    What is the format of the course?

    The content will be a mix of interactive self-paced lectures and live instructor-led training from industry leaders as well as renowned faculty from IIT Roorkee. Linux, Python and Big Data will be provided as self-paced module before the live classes start. Additionally, the program comprises 24*7 support dedicated to solving your academic queries and reinforcing learning. The discussion forum on the CloudxLab website will also facilitate peer-to-peer interactions.

    Do I need to install any software before starting this course?

    No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

    What if I miss any classes in between?

    Recording of every session will be available just after the class, even if you miss the class you can go through the recording and ask your doubts either on our discussion forum or in the next class.

    What are the expected career options after pursuing this course?

    Someone who has successfully completed this course is expected to be able to solve problems more efficiently using some of the latest technologies in the industry. Learners who have completed this course will be a perfect fit for VLSI, Semiconductor, or similar industries.

    What are the prerequisites for this course?

    Basic knowledge of any programming language and Linux will help you in understanding the concepts faster. We will provide access to our self-paced courses on Python and Linux once you sign up for this course.

    What is the eligibility criteria to earn the certificate for the course

    You will be required to have at least 60% attendance in live sessions, complete 1 Capstone Project and 8 Guided projects - Analyse emails from Python, Sentiment Analysis (Hive) from Hadoop, Log Parsing from Spark, 3 mandatory projects from Machine Learning, and 2 mandatory projects from Deep Learning. All the above requirements need to be met within the deadline of the course (11 Months) to be eligible for the certificate from E&ICT Academy, IIT Roorkee.

    What is the validity of the course material?

    We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.

    Is there any EMI option available?

    Yes, for further details please drop a mail to reachus@cloudxlab.com

    May I directly interact with the instructor?

    Yes, every learner can directly ask their questions and discuss his/her query during any of the class lectures. You can also post your query on our discussion forum.

    What is the refund policy?

    We provide a 100% fee refund if the request is raised within the first 2 instructor-led sessions. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. Thereafter, no refund is provided.