Introduction to Artificial Intelligence

• 01 – Decoding Artificial Intelligence
• 02 Meaning, Scope, and Stages Of Artificial Intelligence
• 03 Three Stages of Artificial Intelligence
• 04 Applications of Artificial Intelligence
• 05 Image Recognition
• 06 Applications of Artificial Intelligence – Examples
• 07 Effects of Artificial Intelligence on Society
• 08 Supervises Learning for Telemedicine
• 09 Solves Complex Social Problems
• 10 Benefits Multiple Industries
• 11 Key Takeaways
• Knowledge Check

Fundamentals of Machine Learning

• 01 Fundamentals Of Machine Learning and Deep Learning
• 02 Meaning of Machine Learning
• 03 Relationship between Machine Learning and Statistical Analysis
• 04 Process of Machine Learning
• 05 Types of Machine Learning
• 06 Meaning of Unsupervised Learning
• 07 Meaning of Semi-supervised Learning
• 08 Algorithms of Machine Learning
• 09 Regression
• 10 Naive Bayes
• 11 Naive Bayes Classification
• 12 Machine Learning Algorithms
• 13 Deep Learning
• 14 Artificial Neural Network Definition
• 15 Definition of Perceptron
• 16 Online and Batch Learning
• 17 Key Takeaways
• Knowledge Check

Machine Learning Workflow

• 01 Learning Objective
• 02 Machine Learning Workflow
• 03 Get more data
• 04 Ask a Sharp Question
• 05 Add Data to the Table
• 06 Check for Quality
• 07 Transform Features
• 08 Answer the Questions
• 09 Use the Answer
• 10 Key takeaways
• Knowledge Check

Performance Metrics

• 01 Performance Metrics
• 02 Need For Performance Metrics
• 03 Key Methods Of Performance Metrics
• 04 Confusion Matrix Example
• 05 Terms Of Confusion Matrix
• 06 Minimize False Cases
• 07 Minimize False Positive Example
• 08 Accuracy
• 09 Precision
• 10 Recall Or Sensitivity
• 11 Specificity
• 12 F1 Score
• 13 Key takeaways
• Knowledge Check
• Chat-bot Essentials
• What are frameworks to apply on building a chat-bot
• What are the do’s and dont’s in enhancing the chat-bot
• Machine Learning with Phyton using Scikit-learn(formerly scikits.learn)
• Machine Learning Approach
• Steps 1 and 2
• Steps 3 and 4
• How it Works

• Steps 5 and 6
• Assignment 01
• Demo Assignment 01
• Supervised Learning Model Considerations
• Knowledge Check
• Scikit-Learn
• Knowledge Check
• Key Takeways into Case Study
• How Big Data combines with AI to provide smart recommendations
• Supervised Learning Models – Logistic Regression
• Unsupervised Learning Models
• Pipeline
• Model Persistence and Evaluation
• Knowledge Check
• Natural Language processing with Scikit Learn
• NLP Overview
• NLP Applications
• Knowledge check
• Scenario Based Examination

Final Examination