Introduction to Unsupervised Machine Learning

Data Professionals that are interested in building a foundational understanding of unsupervised machine learning and a skill set to build unsupervised ML models with Python.

Python

• A developed understanding of syntax, data structures, Pandas DataFrames, NumPy and Matplotlib.

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Machine Learning

• Knowledge of linear and logistic regression and basic principles of machine learning, including familiarity with supervised learning algorithms, identifying regression vs. classification ML problems, understanding of loss functions and gradient descent, and model evaluation methods.

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Linear Algebra

• Knowledge of topics such as range, basis, nullspace, eigenvalues, eigenvectors, singular value decomposition, least squares.

Apply various clustering algorithms on data, such as hierarchical and k-means clustering, to identify groupings, perform automatic customer segmentation, spot anomalies, and more.