9 months

Duration

Self-Paced Online

Format

28+

Projects

IIT Roorkee

Certificate

13,500+

Learners

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 IIT Roorkee which you can use for progressing in your career and finding better opportunities.

Program Highlights

  • Certificate of Completion by IIT Roorkee

  • 9 Months of Online Self-Paced Training

  • Work on about 28+ projects to get hands-on experience

  • Timely Doubt Resolution

  • Best In Class Curriculum

  • Cloud Lab Access

Certificate

What is the certificate like?

  • Why IIT Roorkee?

    IIT Roorkee is ranked first among all the IITs AND 20th position globally in citations per faculty. Established in 1847, it's one of the oldest technical institutions in Asia. IIT Roorkee fosters a very strong entrepreneurial culture. Some of their alumni are highly successful as entrepreneurs in the new age digital economy.

  • Why Cloudxlab?

    CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.

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 Self-Paced Training
270
Days of Lab Access
28+
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.

Course on Big Data with Hadoop and Spark

1. Introduction to Hadoop
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
12. Introduction to Spark
1. Apache Spark ecosystem walkthrough
2. Spark Introduction - Why Spark?
13. 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
14. 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
15. 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
16. 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
17. 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?
18. 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
19. 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

Certification Guideline

You will be required to have at least 75% of the course.Along with mandatory Analyse emails from Python, Sentiment Analysis (Hive) from Hadoop, Log Parsing from Spark, 4 mandatory projects from Machine Learning and Deep Learning. All the above requirements need to be met within the deadline of the course (9 Months) to be eligible for the certificate from IIT Roorkee.

No Cost EMI at

99/Month

Or Program Fee 891

  • 9 Months of Self-Paced Learning
  • 270 Days of Online Lab Access
  • 24*7 Support
  • Certificate from IIT Roorkee
Coming Soon »

Testimonials

Frequently Asked Questions

Can I expect any placement support?

Yes, we do offer placement assistance that includes career guidance, resume building tips and mock interviews. Each participant will receive staunch support from the industry mentors, who also direct you through various placement opportunities within the industry. Above all, we are partnered with leading MNC’s that offer placement opportunities to our participants.

Can I get a certificate for the projects completed?

We have created a set of Guided Projects on our platform. You may complete these guided projects and earn the certificate for free. Check it out here

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 is your refund policy?

If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!

What is the validity of 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.