This Artificial Intelligence course provides training in the skills required for a career in AI. You will masterTensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems.

Artificial intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn and adapt.

Introduction to Artificial Intelligence Course Outcomes:

Upon Completion of this course, you will learn:
• The meaning , purpose ,scope ,stages ,applications, and effects of AI
• Fundamental concepts of machine learning and deep learning
• The difference between supervised , semi-supervised and unsupervised learning
• Machine Learning workflow and how to implement the steps effectively
• The role of performance metrics and how to identify their key methods.

The course curriculum of Artificial Intelligence

Lesson 01-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
• Knowledge Check

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
• Naïve Bayes
• Naïve Bayes Classification
• Machine Learning Algorithms
• Deep Learning
• Artificial Neural Network Definition
• Definition of Perceptron
• Online and Batch Learning
• Key Takeaways
• Knowledge Check

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 take always
• Knowledge Check

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
• F1Score
• Key take always
• Knowledge Check