12th September

Batch Starts

Online Instrucor-led






About the Course

Internet is flooded with content on AI, Machine learning, and Deep Learning. Every professional who is aspiring to be an expert in these domains finds it difficult to master the technologies due to a lack of a tutor or mentor who is able to steer them in the right way in their career.

The CloudxLab Masterclass is a series of live online instructor-led sessions from the thought leaders in the industry. The seasoned industry experts will be deep-diving into the concepts of Machine Learning and Deep Learning from the foundations. These interactive live sessions are aimed at providing the best possible mentorship available in these post-sought-after domains.

Furthermore, these sessions complement the recorded course content provided by most content providers and training institutes in the market very well. Learners enrolling in the CloudxLab masterclass expect for a highly interactive instructor-led sessions which are primarily hands-on focussed.

Program Highlights

Placement Eligibility Test

Placement Eligibility Test

Proctored Exams with Deep Learning models with opportunity to get Placed

Timely Doubt Resolution

Timely Doubt Resolution

Get access to community of learners via our discussion forum

Hands-On Project

Hands-On Project

Work on real world projects to get an hands-on experience

Batch Starts on 12th September, 2021

Mentors / Faculty

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

Hands-on Learning

hands-on lab

  • Gamified Learning Platform
    Making learning fun and sustainable

  • Auto-assessment Tests
    Learn by writing code and executing it on lab

  • No Installation Required
    Lab comes pre-installed softwares and accessible everywhere


Foundation Courses

1. Programming Tools and Foundational Concepts
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

Course on Machine Learning

1. Machine Learning Applications & Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Launch, monitor, and maintain the system
4. Explore the data to gain insights
5. Prepare the data for Machine Learning algorithms
6. Explore many different models and short-list the best ones
7. Fine-tune model
3. Classification
1. Training a Binary classification
2. Multiclass,Multilabel and Multioutput Classification
3. Performance Measures
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
4. Training Models
1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
6. Logistic Regression
5. Support Vector Machines
1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
6. Decision Trees
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Gini Impurity or Entropy
6. Regularization Hyperparameters
7. Instability
7. Ensemble Learning and Random Forests
1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting and Stacking
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques

Course on Deep Learning

1. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras with TensorFlow Backend
3. Fine-Tuning Neural Network Hyperparameters
2. Training Deep Neural Networks
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
3. Custom Models and Training with Tensorflow
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
3. Tensorflow Functions and Graphs
4. Loading and Preprocessing Data with TensorFlow
1. Introduction to the Data API
2. TFRecord Format
3. Preprocessing the Input Features
4. TF Transform
5. The TensorFlow Datasets (TDFS) Projects
5. Convolutional Neural Networks
1. The Architecture of the Visual Cortex
2. Convolutional Layer
. Pooling Layer
4. CNN Architectures
5. Classification with Keras
6. Transfer Learning with Keras
7. Object Detection
6. Recurrent Neural Networks
1. Recurrent Neurons and Layers
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. Forecasting a Time Series
6. LSTM Cell
7. GRU Cell
7. Natural Language Processing
1. Introduction to Natural Language Processing
2. Creating a Quiz Using TextBlob
3. Finding Related Posts with scikit-learn
4. Generating Shakespearean Text Using Character RNN
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
7. Attention Mechanisms
8. Recent Innovations in Language Models
8. Autoencoders and GANs
1. Efficient Data Representations
2. Performing PCA with an Under Complete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pre Training Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Generative Adversarial Networks
9. Reinforcement Learning
1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
>5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Deep Q-Learning Variants
10. The TF-Agents Library

Placement Assistance

Placement Eligibility Test

Placement Eligibility Test

We have around 300+ recruitment partners who will be interviewing you based on your performances in PET

Dedicated Job Portal

Dedicated Job Portal

Opportunities from companies who approach us asking for our learner profiles will be posted on our job portal to providevisibility to your profile

Career Guidance Webinars

Career Guidance Webinars

Career Guidance Webinars from seasoned industry experts

Enroll Now

Foundations of Python

Master Class

Batch Starts 12th Sep

8-10 AM IST


9+ Hours

Free Complimentary Access to

Python for Beginners by CloudxLab

Or Program Fee 0

Enroll Now

Foundations of Python

Master Class

Batch Starts 12th Sep

8-10 AM IST


9+ Hours

Foundations of Python

Master Class

Batch Starts 12th Sep

8-10 AM IST


9+ Hours


AI, Machine Learning and Deep Learning

Master Class

Batch Starts on 12th Sep

8-10 AM IST

Saturday, Sunday

180 Days of Lab

84+ Hours

Best Bought With Course on

Artificial Intelligence and Deep Learning by IIT Roorkee

Earn a certifcate by IIT Roorkee.

Or Program Fee 369

Sold Out »


Frequently Asked Questions

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.

Will the course materials be provided while enrolling for the Masterclass?

No. While enrolling for the Masterclass, you'll be given access to the Live training sessions only and not the course material.

Alternatively, you can claim the 20% discount given when the course is purchased along with the masterclass. By doing this, you'll have access to the course material which will be valid for lifetime and get access to the Live Sessions

I have some more questions. Can I talk to someone?

Absolutely! Please contact us here

Will Lab be provided along with the Materclass?

Yes, it will be as follows:

Machine Learning - 90 Days, Deep Learning - 90 Days, Artificial Intelligence and Deep Learning - 180 Days

Please note that for Masterclass that is provided for free (Example - Python for Machine Learning) will have no Lab days included with it. If you're enrolling for the same, you can purchase the lab here

Who will be the course instructors?

The instructors for this course are industry experts having years of experience in mentoring students across the world.

How will be the practical or hands-on be conducted?

We provide the virtual online cloud-based lab with all the software and tools pre-installed so that you can start practicing immediately instead of going through the pain of installing and configuring the tools and software on your local machine.

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 if I miss a class in between?

You will never lose any lecture. You can view the recorded session of the class in your LMS.

Will there be Options to Pay using EMI/Installments

Yes, you can choose to pay by installments on the payment page.

Is there any kind of placement assitance provided?

We provide placement assistance through our job portal. In this way, we provide better visibility for your profile, in the eyes of the recruiter. We also will assist you in resume preparation on request.

How do I claim the discount?

Please drop an email to reachus@cloudxlab.com or contact us here