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Artificial Intelligence

From healthcare to automobile industry, e-commerce to customer service, Artificial Intelligence has significantly impacted our daily lives, and it is predicted that careers in AI will grow significantly by 2025. As enterprises are investing more into artificial intelligence and machine learning, the demand for AI specialists increases in just about every industry. IIHT’s Artificial Intelligence training program is designed to ensure that course participants learn the essential foundations of AI, the programming tools, and the key concepts of Deep Learning. The course structure will also expose students to the best practices and trends while working with Artificial Intelligence.
4 Days
22 Oct - 26 Oct 2018
19.30 - 21.30 (Online)

INR 9999(Amount Exclusive of all taxes and 18% GST)
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  • Basic Programming Languages like C, C++ and Java as they give a basic knowledge of writing and executing codes.
  • High level Programming Language like Python or R
  • Few concepts of Mathematics like Calculus, Probability, Matrices and Statistics

Course Content

Unit 1

  • AI – Introduction, Implementation & Applications
  • Ai v/s ML v/s DL
  • Current Trends & Industrial Applications
  • Market Analysis for AI, ML and DL
  • Skills needed for AI Professional
  • What IT companies are doing?
  • Tools and Techniques for AI
  • Domain specific to Banking, Finance and Consumer Market

Unit 2

  • Environmental Constraints
  • Various Agent Types
  • PEAS Analysis of Problem
  • Process flow for an AI agent
  • Machine Learning Introduction
  • Supervised & Unsupervised Learning
  • Regression & Classification Problems
  • Examples and Algorithms
  • Customer Churn Analysis – possible ML Solutions
  • Image Processing and Computer Vision
  • Demos – Azure ML Studio

Unit 3

  • Natural Language Processing
  • Using NLTK
  • Using Textblob
  • Text Mining and Sentiment Analysis
  • Machine Translation
  • Chatbots
  • Demo: Streaming live tweets and Sentiment Analysis
  • TF-IDF
  • Word2vec
  • Word Embedding

Unit 4

  • Prioritization of AI Application
  • Request Response architecture with Trained model
  • Image Processing
  • Computer Vision
  • Current Advancements
  • Deep Learning
  • Deep Learning Applications and use cases
  • Demos