Artificial Intelligence Certified Course

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. 

AI is a subfield of computer science and is based on the idea of creating machines that can simulate human intelligence. AI systems are designed to learn from experience and improve their performance over time without being explicitly programmed. 

The field of AI is divided into various subfields, such as machine learning, natural language processing, computer vision, robotics, and expert systems. AI has a wide range of applications in industries such as healthcare, finance, transportation, and entertainment. 

Some of the key techniques used in AI include neural networks, deep learning, reinforcement learning, and natural language processing. With the continued advancement of AI technologies, there is a growing interest in exploring the potential of AI to solve complex problems and create new opportunities for innovation.

What Will You Learn? 

• 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
• 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
• 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
• 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