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).
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.
- 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.
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
- 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