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Intro to Machine Learning - Coming Soon!

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This course will take a practical approach to learning machine learning. We'll cover the basics of why things are happening (like why and how the algorithms are able to adapt to (learn from) the data), but we'll spend the majority of the time applying time-tested machine learning libraries and methods to real data. We'll then analyze the results and demonstrate how to cacluate the accuracy of the methods applied. The methods we'll study include:

  • Linear Regression

    • Of a single variable

    • Multivariate

  • Logistic regression

    • Intro to classification

    • Classification by logistic regression

  • Trees

    • A single decision tree; hardcoded and machine learning

    • A forest of trees: random forest classifiers

  • Unsupervised learning

    • Kmeans

    • DBSCAN

    • PCA
  • Brief intro to Neural Networks

    • NNs using PyTorch


To take this course, you'll need a basic understanding of Python. It'll help to have seen the very basics of linear algebra before, but it's not necessary. You will need to have the following dependencies installed on your machine: Python3, Pandas, Numpy, scikit-learn, PyTorch (v0.4 or later), and matplotlib.

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