Artificial Intelligence

  • Batch start date 06th June 2020
  • Learning hours 180 Hrs
  • Batch size 30 Participants
  • Learning Schedule Wednesday, Saturday & Sunday

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.

Minimum eligibility

Working knowledge of the basics of Statistics & Mathematics

Course Fee

An overall fee of ₹ 1 Lakh including taxes

Course highlights

World-class technical mentors

World-class
technical mentors

Relevant & interactive pedagogy

Relevant &
interactive pedagogy

Adaptive & contemporary curriculum

Adaptive &
contemporary curriculum

Immersive learning experience

Immersive learning
experience

Hands-on, real-world case studies & projects

Hands-on, real-world
case studies & projects

Exclusive access to the Tech Lounge

Exclusive access
to the Tech Lounge

Mentoring sessions from Silicon Valley

Mentoring sessions
from Silicon Valley

In-house career coaching

In-house career coaching

Placement assurance

Placement assurance

Course curriculum

  • 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.
  • 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).
  • 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.
  • 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 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.

Mentors for the course

Moenes Iskarous

Moenes Iskarous

View profile
Bipin Thomas

Bipin Thomas

View profile
Jeremiah Liou

Jeremiah Liou

View profile
Hari Krishna Jeedipalli

Hari Krishna Jeedipalli

View profile
Rahul Bhuman

Rahul Bhuman

View profile
Madhusudhan Reddy

Madhusudhan Reddy

View profile
Sudhakar Alla

Sudhakar Alla

View profile
Shweta Polamreddy

Shweta Polamreddy

View profile
Vijaysekhar Chellaboina

Vijaysekhar Chellaboina

View profile
Sai Charan Tej Kommuri

Sai Charan Tej Kommuri

View profile
Rajeev Kumar

Rajeev Kumar

View profile
Lavi Nigam

Lavi Nigam

View profile
Kevin Vivian

Kevin Vivian

View profile
Solomon Darwin

Solomon Darwin

View profile
Saumya Joshi

Saumya Joshi

View profile