AI & Machine Learning

Artificial Intelligence has become a powerful driving force in a wide range of industries, helping people and businesses create exciting, innovative products and services, enable more informed business decisions, and achieve key performance goals.

The median salary of an AI engineer in the US is $171,715 (Source: Datamation).

By 2022, the AI market will grow at a CAGR of 53.25 per cent, and an estimated. 2.3 million Jobs will be created in the AI field by 2020 (Source: Gartner).

Program Objective
This course will give you a look at the booming field of AI and show you how AI can help drive business value. The course covers basic concepts, terminologies, scope and stages of artificial intelligence and their effect on real-world business processes.

By the end of the course, you will be able to clearly define various supervised and unsupervised AI algorithms, apply machine learning workflow to solve business problems and measure ROI based on performance metrics.

Target Audience
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 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.
There is no prerequisite for this course. It does not require programming or IT background, making it ideal for professionals in all walks of corporate life.

Program Duration
40 hours

Course Outline
Introduction to Artificial Intelligence

Course Introduction
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
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
Naive Bayes
Naive Bayes Classification
Machine Learning Algorithms
Deep Learning
Artificial Neural Network Definition
Definition of Perceptron
Online and Batch Learning
Machine Learning Workflow

Get more data
Ask a Sharp Question
Add Data to the Table
Check for Quality
Transform Features
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
Recall or Sensitivity
F1 Score
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