Artificial Intelligence

1965 Ratings (4.9)

This Introduction to AI provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised, and reinforcement learning; be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.

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What you will learn:

By the end of this AI training, you will be able to accomplish the following:

  • Understand the meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
  • Design and build your own intelligent agents, applying them to create practical Artificial Intelligence projects, including games, machine learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, and agent decision-making functions
  • Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions
  • Learn how to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
  • Master the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engine, and time series modeling
  • Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
  • Master advanced topics in Artificial Intelligence, such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces

Course Contents

Introduction to Python
  • Concepts of Python programming
  • Configuration of Development Environment
  • Variable and Strings
  • Functions, Control Flow and Loops
  • Tuple, Lists and Dictionaries
  • Standard Libraries
Data Science Fundamentals
  • Introduction to Data Science
  • Real world use-cases of Data Science
  • Walkthrough of data types
  • Data Science project lifecycle
Introduction to NumPy
  • Basics of NumPy Arrays
  • Mathematical operations in NumPy
  • NumPy Array manipulation
  • NumPy Array broadcasting
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Course Prerequisites

Participants in this program should have:
  • An understanding of the fundamentals of Python programming
  • Basic knowledge of statistics

Who should attend?

  • Developers aspiring to be an Artificial Intelligence Engineer or Machine Learning Engineer
  • Analytics Managers who are leading a team of analysts
  • Information Architects who want to gain expertise in Artificial Intelligence algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in Artificial Intelligence and machine learning
  • Experienced professionals who would like to harness Artificial Intelligence in their fields to get more insight

Number of Hours: 60hrs

Keyfeatures

  • One to One Training
  • Online Training
  • Fastrack & Normal Track
  • Resume Modification
  • Mock Interviews
  • Video Tutorials
  • Materials
  • Real Time Projects
  • Virtual Live Experience
  • Preparing for Certification

FAQs

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TechyEdz Software Specialization in developing customized suite of HR consulting solutions based on the operational models of our clients with a special focus on small & Large Enterprises. We bring practical, results driven HR Practice to our clients business that would help them retain people, Improve business productivity and performance of employee.