Introduction to Artificial Intelligence

Overview:

Introduction:

The Introduction to Artificial Intelligence training program provides a fundamental understanding of AI concepts and applications. Participants explore AI's history, current capabilities, and potential impact across industries. Through theoretical learning and practical exercises, individuals establish a foundational knowledge base for further exploration in AI.

Program Objectives:

At the end of this program the participants will be able to:

• Understand the foundational concepts of Artificial Intelligence.

• Explore the diverse applications of AI in business and industry.

• Develop proficiency in core mathematical concepts and programming languages relevant to AI.

• Gain practical skills and knowledge to embark on further exploration and application of AI technologies.

Targeted Audience:

• Professionals seeking to enhance their understanding of AI concepts and applications.

• Business leaders and decision-makers looking to leverage AI for strategic advantage.

Program Outlines:

Unit 1:

Introduction to Artificial Intelligence:

• Course Introduction. 

• Introduction.

Unit 2:

Decoding Artificial Intelligence:

• Decoding Artificial Intelligence and its meaning, scope, and stages.

• Exploring the three stages of Artificial Intelligence and its applications.

• Investigating applications like image recognition and examples of AI's impact across industries.

• Analyzing the effects of Artificial Intelligence on society, including its role in telemedicine and solving complex social problems.

• Understanding the benefits AI offers multiple industries and 11 key takeaways.

• Concluding with a knowledge check to reinforce learning and comprehension.

Unit 3:

Fundamentals of Machine Learning and Deep Learning:

• Exploring the meaning of Machine Learning and its relationship with Statistical Analysis

• Understanding the process and types of Machine Learning, including Unsupervised and Semi-supervised Learning

• Delving into Machine Learning algorithms such as Regression, Naive Bayes, and Deep Learning

• Defining concepts like Artificial Neural Networks, Perceptron, and Online vs. Batch Learning

• Highlighting key algorithms and their applications in Machine Learning

• Concluding with key takeaways and a knowledge check to reinforce understanding.

Unit 4:

Machine Learning Workflow:

• Learning Objective: Understand the Machine Learning Workflow.

• Acquire more data and formulate sharp questions for analysis.

• Add and assess data quality in the dataset.

• Transform features and extract meaningful insights to answer questions.

• Utilize the obtained answers effectively and reinforce learning with key takeaways and a knowledge check.

Unit 5:

Performance Metrics:

• Understanding the need for Performance Metrics and key methods employed.

• Exploring the components of a Confusion Matrix with an example.

• Identifying terms associated with the Confusion Matrix and strategies to minimize false cases.

• Examining metrics like Accuracy, Precision, Recall (Sensitivity), Specificity, and F1 Score.

• Concluding with key takeaways and a knowledge check to reinforce understanding.