World-Class Ed-Tech Institute For Cutting-Edge Training In AI & Blockchain

Artificial Intelligence Immersive

Discover the ever-growing potential of AI through a comprehensive curriculum, interactive learning experience & hands-on industry-specific case studies.

Our program has been designed and taught by the industry experts from Silicon Valley, California with learning content and assessments created by mentors from the University of California (San Diego), University of California (Santa Cruz) and other renowned practising data scientists and Artificial Intelligence experts.

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Course curriculum

Overview of AI and Machine Learning Engineering Stack

Learn about the different branches of AI, and the difference between AI, Machine Learning and Data Science.
Get introduced to the tools and libraries used in the worlds of data science and engineering.
Learn software engineering best practices that apply to AI/ML practitioners.

Begin with Python

In this module, you will learn the essential foundations of Python as needed for AI programming.
You will learn the critical Python data types, strings, Object-Oriented Programming, data structures, and libraries (Python, NumPy, Pandas, PyTorch).

Big data for Artificial Intelligence

In this module, you will get answers to important questions such as: what is Big Data? How do we tackle Big Data? Why is the combination of AI and Big data critical?
You will learn You will be guided through the basics of using Hadoop with MapReduce, Spark.

Math – Linear Algebra, Calculus, Statistics

Mathematics plays an important role as it builds the foundation for AI programming. This module will help you get you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Deep Learning.
The module breaks the difficult mathematical concepts into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory

Machine Learning concepts

Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention.
In this module, you will learn with a broad cross-section of models and algorithms for machine learning, and prepare you for the industry application of machine learning techniques.
You will explore the most important supervised and unsupervised machine learning algorithms. You also will learn when and how to implement these algorithms at scale.

A Deep Dive into Deep Learning

Deep Learning is one of the most highly sought-after skills in technology.
In this module, you will establish a thorough foundation in deep learning and build real-world applications.
You will learn about neural network principles and engineering frameworks such as Keras and PyTorch
And learn how to lead successful deep learning projects. You will learn about Convolutional networks, RNNs, LSTM

Natural Language Processing

Learn NLP with foundations in text data, including how to clean and process it, and how to extract insights from text and conversations.
You will work through a detailed case study and solve a real NLP problem using deep learning and other techniques.

AI for Robotics

This module will introduce you to basic methods in Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.
Included are programming examples and assignments that will apply these methods in the context of building self-driving cars/robots moving in two dimensions.

Building and Deploying Large-Scale AI Systems

Apply what you’ve learned in this course by developing a realistic, large-scale, deployed AI system.
Learn about common tools and techniques, deploying ML applications, real-time data processing, and making your application available via API or a web service.

Capstone Project

You will build and deploy Large-Scale AI Systems. You will apply what you’ve learned in this course by developing a realistic, large-scale, deployed AI system.
You will learn about common tools and techniques, deploying ML applications, real-time data processing, and making your application available via API or a web service.
Build a realistic, complete, large-scale API application that’s available to use via an API, a web service, or — optionally — a website.
You’ll have free access to a cloud-based engineering environment, which will support all of the standard tools and libraries.

Artificial Intelligence

Artificial Intelligence Immersive

Minimum eligibility

Course Fee

Course highlights

Course curriculum

Overview of AI and Machine Learning Engineering Stack

Begin with Python

Big data for Artificial Intelligence

Math – Linear Algebra, Calculus, Statistics

Machine Learning concepts

A Deep Dive into Deep Learning

Natural Language Processing

AI for Robotics

Building and Deploying Large-Scale AI Systems

Capstone Project

Case based Final Certification Examination

Mentors for the course

Moenes Iskarous

Bipin Thomas

Jeremiah Liou

Hari Krishna Jeedipalli

Rahul Bhuman

Madhusudhan Reddy

Sudhakar Alla

Shweta Polamreddy

Vijaysekhar Chellaboina

Sai Charan Tej Kommuri

Rajeev Kumar

Lavi Nigam

Kevin Vivian

Solomon Darwin

Saumya Joshi