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Introduction to Artificial Intelligence

 66,000.0  50,000.0

This Introduction to AI course provides an overview of AI concepts and workflows, machine learning and 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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Description

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Program Overview:

This Introduction to AI course provides an overview of AI concepts and workflows, machine learning and 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.

Program Features:

  • 3.5 hours of enriched learning
  • High quality e-learning content
  • Lifetime access to self-paced learning
  • Industry recognized course completion certificate

Prerequisites:

There are no prerequisites for opting for this Introduction to Artificial Intelligence course. It does not require programming or IT background, making it ideal for professionals from all walks of corporate life.

Target Audience:

This Artificial Intelligence Introduction course does not require programming or IT background, making it well-suited for the following audience :

  • Analytics managers who are leading a team of analysts
  • Developers aspiring to be an artificial intelligence engineer or machine learning engineer
  • Information architects who want to gain expertise in AI algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence Graduates looking to build a career in artificial intelligence or machine learning

Key Learning Outcomes:

  • When you complete this Introduction to Artificial Intelligence course, you will be able to accomplish the following:
  • The meaning, purpose, scope, stages, applications, and effects of AI
  • Fundamental concepts of machine learning and deep learning
  • The difference between supervised,semi-supervised and unsupervised learning
  • Machine Learning workflow and how to implement the steps effectively
  • The role of performance metrics and how to identify their key methods

Certification Details and Criteria:

  • Complete the online self-learning course
  • Complete the course-end assessment with a minimum 80% score

Course Curriculum:

Lesson 01 – Decoding Artificial Intelligence

    • Decoding Artificial Intelligence
    • Meaning, Scope, and Stages Of Artificial Intelligence
    • Three Stages of Artificial Intelligence
    • Applications of Artificial Intelligence
    • Image Recognition
    • Applications of Artificial Intelligence – Examples
    • Effects of Artificial Intelligence on Society
    • Supervises Learning for Telemedicine
    • Solves Complex Social Problems
    • Benefits Multiple Industries
    • Key Takeaways

Lesson 02 – Fundamentals of Machine Learning and Deep Learning

    • Fundamentals Of Machine Learning and Deep Learning
    • Meaning of Machine Learning
    • Relationship between Machine Learning and Statistical Analysis
    • Process of Machine Learning
    • Types of Machine Learning
    • Meaning of Unsupervised Learning
    • Meaning of Semi-supervised Learning
    • Algorithms of Machine Learning
    • Regression
    • Naive Bayes
    • Naive Bayes Classification
    • Machine Learning Algorithms
    • Deep Learning
    • Artificial Neural Network Definition
    • Definition of Perceptron
    • Online and Batch Learning
    • Key Takeaways

Lesson 03 – Machine Learning Workflow

    • Learning Objective
    • Machine Learning Workflow
    • Get more data
    • Ask a Sharp Question
    • Add Data to the Table
    • Check for Quality
    • Transform Features
    • Answer the Questions
    • Use the Answer Key Takeaways

Lesson 04 – Performance Metrics

    • Performance Metrics
    • Need For Performance Metrics
    • Key Methods Of Performance Metrics
    • Confusion Matrix Example
    • Terms Of Confusion Matrix
    • Minimize False Cases
    • Minimize False Positive Example
    • Accuracy
    • Precision
    • Recall Or Sensitivity
    • Specificity
    • F1 Score
    • Key Takeaways

Additional information

Course Access Duration

Life Time

Learning Method

Online Self-study

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