What you'll learn

AB Testing in R

Al Ethics

Artificial Intelligence (Al) Strategy

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

Requirements

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.

Prior 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.