Machine Learning: Concepts and Applications

Instructor: Dr. Nick Feamster

Skills you'll gain

  •   Decision Tree Learning
  •   Statistical Methods
  •   Artificial Neural Networks
  •   Scikit Learn (Machine Learning Library)
  •   Deep Learning
  •   Machine Learning
  •   Random Forest Algorithm
  •   Regression Analysis
  •   Tensorflow
  •   Machine Learning Algorithms
  •   Classification And Regression Tree (CART)
  •   Feature Engineering
  •   Applied Machine Learning
  •   Supervised Learning
  •   Unsupervised Learning
  •   Dimensionality Reduction
  •   Pandas (Python Package)
  • There are 9 modules in this course

    A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.

    Least Squares and Maximum Likelihood Estimation

    Basis Functions and Regularization

    Model Selection and Logistic Regression

    More Classifiers: SVMs and Naive Bayes

    Tree-Based Models, Ensemble Methods, and Evaluation

    Clustering Methods

    Dimensionality Reduction and Temporal Models

    Deep Learning

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