Description
ChatGPT Masterclass: The Guide to AI and Tech, Prompt 23. ChatGPT Masterclass: The Guide to AI and Tech, 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 if there is a significant difference in performance metrics and if the metrics 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 testing answers, key considerations for A/B testing, how to answer existing questions, and how to visualize data. You’ll also learn how to determine the sample size needed for an experiment, conduct appropriate analyses for the available data and hypotheses, ensure that the results can be considered with confidence, and how to interpret the results presented to the audience without a statistical background. This course covers parametric and nonparametric A/B tests such as the t-test, Mann-Whitney U test, chi-square test of independence, Fisher’s exact test, and Pearson and Spearman correlation. In addition, performance analysis for each test is reviewed. The Ethics of Artificial Intelligence 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 for promoting fair and equitable AI systems, methods for minimizing bias, and approaches for solving important problems and building user trust. In this course, you will learn the fundamentals of ethical AI and increase your understanding of common challenges and opportunities in the field of AI ethics. Through hands-on exercises, you will develop skills for creating ethical AI.
What you’ll learn in the ChatGPT Masterclass: The Guide to AI and Prompt Engineering 23
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Strategy for Artificial Intelligence (AI).
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Data preparation in Excel
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Data visualization in Excel
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Deep Learning for Text with PyTorch
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Dimension reduction in R
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End-to-end machine learning
This course is suitable for people who
- Marketing professionals
- product manager
- Data scientist and artificial intelligence developer
- Managing Director
- Company Director
- Technology Consultant
- Economic analysts
- Entry-level data scientist
- Economic analysts
- Financial analysts
- Data analysts
- Researchers
- Natural language processing (NLP) engineers.
- Artificial intelligence researcher
- Data Scientist
- statistics
- Data engineers
- Data Scientist
Details of the ChatGPT Masterclass Course: The Guide to AI and Prompt Engineering 23
- Editor: Udemy
- Teacher: CodeLearnAcademy
- Training level: beginner to advanced
- Training duration: 9 hours and 22 minutes
- Number of courses: 122
Course topics on 1/2024
Prerequisites for the ChatGPT Masterclass: The Guide to AI and Prompt Engineering 23
- Basic understanding of statistics and hypothesis testing.
- Familiarity with experimental design principles.
- Knowledge of the domain or industry for effective testing.
- Knowledge of ethics or philosophy is an advantage.
- Familiarity with ethical considerations in technology.
- Understanding 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.
- Knowledge of spreadsheet programs such as Excel.
- Familiarity with basic statistical analysis.
- Knowledge of using Excel for data entry and processing.
- Basic knowledge of data cleaning and formatting.
- Understanding common data quality issues.
- Knowledge of Excel for data visualization.
- Understanding the principles of effective data visualization.
- Knowledge of different chart types and their uses.
- Familiarity with the Python programming language.
- Basic understanding of machine learning concepts.
- Previous experience with neural networks is an advantage.
- Knowledge of the R programming language.
- Understanding of data preprocessing and feature engineering.
- Knowledge of the challenges of high-dimensional data.
- Knowledge of a programming language such as Python.
- Understanding machine learning algorithms and models.
- Knowledge of the entire machine learning pipeline from data preparation to model deployment.
Course pictures
Sample video of the course
installation Guide
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Quality: 720p
Download link
free download software
Size
2.3GB