Instructor: Roland Doku, Ph.D. Part II will build on concepts discuss in Part I. We will go through examples of how to conduct an A/B test on a real-world use case, covering all necessary processes from designing the experiment to interpreting the results.  Discuss situations when A/B testing doesn’t work. By the end of the […]

Instructor: Roland Doku, Ph.D. Part II will build on concepts discuss in Part I. We will go through examples of how to conduct an A/B test on a real-world use case, covering all necessary processes from designing the experiment to interpreting the results.  Discuss situations when A/B testing doesn’t work. By the end of the […]

Instructor: TBD. Topics to be determined.

Instructor: TBD. Topics to be determined.

Instructor: Ebelechukwu Nwafor, PhD This two-part module delves into the core principles of machine learning. In Part I, students will learn about supervised learning techniques, covering linear regression, classification, and model evaluation metrics. They will explore foundational algorithms, including decision trees, support vector machines, and k-nearest neighbors, while focusing on applications in real-world scenarios.

Instructor: Ebelechukwu Nwafor, PhD This two-part module delves into the core principles of machine learning. In Part I, students will learn about supervised learning techniques, covering linear regression, classification, and model evaluation metrics. They will explore foundational algorithms, including decision trees, support vector machines, and k-nearest neighbors, while focusing on applications in real-world scenarios.

Instructor: Ebelechukwu Nwafor, PhD Part II builds on these basics from Part I, introducing unsupervised learning techniques such as clustering and dimensionality reduction. The module will also cover key concepts like overfitting, and model selection. By the end, students will understand both theoretical and practical aspects of machine learning, with hands-on experience in building and […]

Instructor:  Ebelechukwu Nwafor, PhD Part II builds on these basics from Part I, introducing unsupervised learning techniques such as clustering and dimensionality reduction. The module will also cover key concepts like overfitting, and model selection. By the end, students will understand both theoretical and practical aspects of machine learning, with hands-on experience in building and […]