Ibrahim Alabi, a Ph.D. student in Computing at Boise State University, begins by introducing machine learning and explaining the set of techniques for finding patterns in data. The two main types of machine learning problems discussed are regression, which predicts continuous values, and classification, which categorizes data.
Alabi discusses linear regression and its limitations as well as logistic regression and how it is used as an alternative for non-linear data. Alabi also demonstrates using the Python library Scikit-Learn for machine learning and predicting life expectancy.
The presentation also focused on cluster analysis for unsupervised learning and dimensionality reduction techniques like PCA and t-SNE for simplifying high-dimensional data.
Finally, the basics of neural networks, deep learning, and perceptron, the core unit of neural networks, were explained. Alabi concludes by highlighting important ethical considerations and implications of deploying machine learning systems.
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Introduction to Machine Learning with Scikit Learn Video Transcript