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Digital Innovation and Transformation
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:
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Understand the foundational concepts of Artificial Intelligence.
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Explore the diverse applications of AI in business and industry.
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Develop proficiency in core mathematical concepts and programming languages relevant to AI.
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Gain practical skills and knowledge to embark on further exploration and application of AI technologies.
Targeted Audience:
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Professionals seeking to enhance their understanding of AI concepts and applications.
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Business leaders and decision-makers looking to leverage AI for strategic advantage.
Program Outlines:
Unit 1:
Introduction to Artificial Intelligence:
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Course Introduction.
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Introduction.
Unit 2:
Decoding Artificial Intelligence:
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Decoding Artificial Intelligence and its meaning, scope, and stages.
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Exploring the three stages of Artificial Intelligence and its applications.
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Investigating applications like image recognition and examples of AI's impact across industries.
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Analyzing the effects of Artificial Intelligence on society, including its role in telemedicine and solving complex social problems.
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Understanding the benefits AI offers multiple industries and 11 key takeaways.
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Concluding with a knowledge check to reinforce learning and comprehension.
Unit 3:
Fundamentals of Machine Learning and Deep Learning:
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Exploring the meaning of Machine Learning and its relationship with Statistical Analysis
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Understanding the process and types of Machine Learning, including Unsupervised and Semi-supervised Learning
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Delving into Machine Learning algorithms such as Regression, Naive Bayes, and Deep Learning
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Defining concepts like Artificial Neural Networks, Perceptron, and Online vs. Batch Learning
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Highlighting key algorithms and their applications in Machine Learning
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Concluding with key takeaways and a knowledge check to reinforce understanding.
Unit 4:
Machine Learning Workflow:
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Learning Objective: Understand the Machine Learning Workflow.
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Acquire more data and formulate sharp questions for analysis.
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Add and assess data quality in the dataset.
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Transform features and extract meaningful insights to answer questions.
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Utilize the obtained answers effectively and reinforce learning with key takeaways and a knowledge check.
Unit 5:
Performance Metrics:
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Understanding the need for Performance Metrics and key methods employed.
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Exploring the components of a Confusion Matrix with an example.
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Identifying terms associated with the Confusion Matrix and strategies to minimize false cases.
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Examining metrics like Accuracy, Precision, Recall (Sensitivity), Specificity, and F1 Score.
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Concluding with key takeaways and a knowledge check to reinforce understanding.