Artificial Intelligence

This course introduces the basic concepts and techniques of Artificial Intelligence. Artificial intelligence is the sub-area of computer science devoted to creating software and hardware to get computers to do things that would be considered intelligent as if people did them. Artificial intelligence has had an active and exciting history and is now a reasonably mature area of computer science.

3 Courses |
  • 120 hours
  • End module assessments
  • Dedicated Learning Advisors for training
  • 24x7 Online LMS

Your learning path

Data Science with Python
Machine learning
Deep Learning

Total Cost :

4,500 (Incl of all taxes)

Course Fee: This is a paid course.

Suitable Job Roles

  • Data Analyst
  • Programmer - R & D
  • Software Developer
  • Lead Engineer - Analytics
  • Professor

Data Science with Python

Python is a powerful high-level, object-oriented programming language. It has wide range of applications from Web development, scientific and mathematical computing. The syntax of the language is clean and length of the code is relatively short. It allows you to think about the problem rather than focusing on the syntax. SQLite is one free lightweight database commonly used by Python programmers to store data. Many highly trafficked websites, such as YouTube, are created using Python.
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Machine learning

Machine learning is the science of getting computers to act without being explicitly programmed, Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. Machine learning algorithms allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.
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Deep Learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. Deep learning refers to the number of layers through which the data is transformed.
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