Excel to MySQL: Analytic Techniques for Business Specialization

Unsupervised ML: Customer Market Segmentation

Gain expertise in leveraging machine learning for marketing transformation, applying unsupervised models like PCA and K-Means, understanding the theory behind k-means clustering and PCA, and determining the optimal number of clusters using the elbow method.

Foundations of Data Science: K-Means Clustering in Python

Leverage Python programming skills to develop and analyze comprehensive clustering procedures, thereby mastering the fundamental concepts and operations of data clustering, with a particular emphasis on the K-means algorithm.

Interpretable ml applications: Part 5

With an emphasis on fairness measurement methods, the course teaches students how to use the Aequitas Tool to identify bias in machine learning models.

Linear Algebra in ML & Data Science

Apply linear algebra concepts like linear independence, rank, singularity, eigenvalues, and eigenvectors to analyze data and solve machine learning problems using standard vector and matrix operations.

Managing ML Projects with Google Cloud

This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.