Artificial Intelligence

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. 

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