Certification On Artificial Intelligence & Machine Learning

This course is an Introduction to Artificial Intelligence (AI) and is designed to help learners decode the mystery of artificial intelligence and its business applications. This AI for Beginners course provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics.

This Introduction to 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.

Outline:

• Lesson 01 – Decoding 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
• Lesson 02 – Fundamentals Of Machine Learning & Deep 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
• Lesson 03 – 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
• 11 Key takeaways
• Knowledge Check
• Lesson 04 – 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