Udemy – ChatGPT Masterclass: The Guide to AI & Prompt Engineering 23 2023-12

Udemy – ChatGPT Masterclass: The Guide to AI & Prompt Engineering 23 2023-12 Downloadly IRSpace

Apr 17, 2025 - 18:50 Updated: Apr 21, 2025 - 08:44

0 0

Udemy – ChatGPT Masterclass: The Guide to AI & Prompt Engineering 23 2023-12

ChatGPT Masterclass: The Guide to AI & Engineering Prompt 23. ChatGPT Masterclass: The Guide to AI & Engineering Prompt 23.

A/B Testing in R is a course offered by Code Learn Academy that focuses on exploring A/B testing using the R programming language. A/B testing is a common experimental design used in both industry and academia to study human behavior. These tests compare two variables to determine whether there is a significant difference in the performance measures and whether the measures are significantly different in a meaningful way. By mastering A/B testing and interpreting results, you can make data-driven decisions and predictions. In this course, you’ll learn what questions A/B tests answer, essential considerations for A/B testing, how to answer existing questions, and how to visualize data. You will also learn how to determine the sample size needed for an experiment, perform appropriate analyzes for the data and hypotheses available, ensure that the results can be considered with confidence, and interpret the results. without statistical background presented to the audience. This course includes parametric and non-parametric A/B tests such as t-test, Mann-Whitney U test, chi-square independence test, Fisher’s exact test and Pearson and Spearman correlation. In addition, the power analysis for each test will be reviewed. Artificial Intelligence Ethics course is published by Code Learn Academy. This introductory course on the ethics of artificial intelligence provides an overview of ethical considerations in the rapidly evolving field of artificial intelligence. It spans industry, policy, academia, and society at large, and covers principles of AI ethics, strategies to foster fair and equitable AI systems, methods to minimize biases, and approaches to address key issues and build user trust. Gives. During this course, you will learn the basics of ethical AI and expand your understanding of common challenges and opportunities in the field of AI ethics. Through hands-on exercises, you’ll develop skills in creating ethical AI.

What you will learn in ChatGPT Masterclass: The Guide to AI & Prompt Engineering 23

AB test in R
Akhlaq Al
Artificial intelligence strategy (Al).
Data fluency
Data preparation in Excel
Data visualization in Excel
Deep learning for text with PyTorch
Dimensionality reduction in R
End-to-end machine learning

This course is suitable for people who

Marketing professionals
Product managers
Data scientists and artificial intelligence developers
Business leaders
Business managers
Technology consultants
Business analysts
Entry-level data scientists
Business analysts
Financial analysts
Data analysts
researchers
Natural Language Processing (NLP) engineers.
Artificial intelligence researchers
Data scientists
Statisticians
data engineers
Data scientists

ChatGPT Masterclass Course Details: The Guide to AI & Prompt Engineering 23

Publisher: Udemy
Instructor: Code Learn Academy
Training level: beginner to advanced
Training duration: 9 hours and 22 minutes
Number of courses: 122

Course topics on 1/2024

Prerequisites for ChatGPT Masterclass: The Guide to AI & Prompt Engineering 23

Basic understanding of statistics and hypothesis testing.
Familiarity with experimental design principles.
Knowledge of the domain or industry for effective testing.
Background in ethics or philosophy is beneficial.
Familiarity with ethical considerations in technology.
Understanding of the societal impact of AI.
Basic knowledge of AI concepts and applications.
Understanding of business strategy and objectives.
Familiarity with industry trends in AI adoption.
Basic understanding of data concepts and terminology.
Proficiency in using spreadsheet software like Excel.
Familiarity with basic statistical analysis.
Proficiency in using Excel for data entry and manipulation.
Basic knowledge of data cleaning and formatting.
Understanding of common data quality issues.
Proficiency in Excel for data visualization.
Understanding of principles of effective data visualization.
Knowledge of different chart types and when to use them.
Familiarity with Python programming language.
Basic understanding of machine learning concepts.
Previous experience with neural networks is beneficial.
Proficiency in the R programming language.
Understanding of data preprocessing and feature engineering.
Knowledge of the challenges of high-dimensional data.
Proficiency in a programming language like Python.
Understanding of machine learning algorithms and models.
Knowledge of the complete machine learning pipeline from data preparation to model deployment.