– Artificial Intelligence Introduction

– Decoding Artificial Intelligence
– Meaning, scope and stages of Al
– Three stages of Al
– Application of Al
– Image Recognition
– Application of Al – Examples
– Effects of Al on society
– Supervises learning for Telemedicine

– Fundamentals of Machine Learning and Deep learning
– Meaning of Machine learning
– Relationship between Machine learning and statistical Analysis
– Process of Machine learning
– Meaning of unsupervised learning
– Meaning of Semi supervised learning
– Algorithms of Machine learning
– Naïve Bayes
– Naïve Bayes Classification
– Machine learning Algorithms
– Deep learning
– Artificial Neural Network Definition
– Definition of Perceptron

– Need for performance metrics
– Key methods of performance metrics – Confusion matrix example
– Terms of Confusion matrix
– Minimize False Positive example
– Accuracy
– Precision
– Recall or Sensitivity
– Specificity
– F1 score

A: In an AI course, you will learn the fundamentals of AI, including machine learning, deep learning, natural language processing, computer vision, and data analysis. You will learn about various AI algorithms, techniques, and tools used for tasks such as data preprocessing, feature selection, model training, evaluation, and deployment. The course may also cover ethical considerations and AI applications.

A: Prerequisites for AI courses can vary depending on the course level and content. Some introductory courses may not have specific prerequisites and assume minimal programming knowledge. However, advanced courses may require a solid understanding of programming concepts, mathematics (such as linear algebra and calculus), and statistics. It’s advisable to review the course requirements before enrolling.

A: Yes, there are many online AI courses available. These courses offer the flexibility of self-paced learning and can be accessed from anywhere with an internet connection. Online platforms often provide video lectures, coding exercises, interactive quizzes, and practical projects to facilitate learning.

A: Reputable AI courses often provide hands-on exercises, coding assignments, and real-world projects to provide practical experience. These activities allow you to apply AI algorithms and techniques, work with datasets, train and evaluate models, and gain practical experience in AI development.

A: When choosing an AI course, consider factors such as the course content, instructor expertise, hands-on practice, industry relevance, course reviews, and cost. It’s important to select a course that aligns with your goals, interests, and desired level of proficiency in AI. Additionally, ensure that the course covers relevant AI algorithms, techniques, and tools used in the industry.