Hours of Training











About the Course


Program Highlights

  • 230+ Hours of Training

  • Work on about 20+ projects to get hands-on experience

  • Timely Doubt Resolution

  • Best In Class Curriculum

  • Certificate of Completion by CloudxLab

  • Cloud Lab Access


What is the certificate like?

  • 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

Course Creators

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


Hours of Online Training
Days of Lab Access
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.


1. Introduction to DevOps
1. What is DevOps ?
2. 10,000 foot view
3. Why DevOps ?
4. Dev-Test-Deploy
5. DevOps Principles
6. DevOps Toolchain
7. Overview of DevOps Tools
8. Co-relation between Agile and DevOps
9. Categories of DevOps Tools
10. Summary


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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

Scholarship Details

  1. Based on Online Scholarship Test
    1. Scholarship Test for the Batch will be conducted (Dates to be announced soon) . Format of the test will be MCQs. Test link will be circulated before one day of the test date. Top 20 Users will get 10% discount, then 5% discount for next 10 Users.
  2. If you receive the Scholarship, you will need to make the payment and then the amount will be refunded.

No Cost EMI at


Or Program Fee 1799

  • 11 Months of Blended Learning
  • 330 Days of Online Lab Access
  • 24*7 Support
  • Batch Starts on March 21, 2021
  • Certificate from E&ICT Academy, IIT Roorkee
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  • Please note that there is an additional 5% off for one-time payment
  • Testimonials