Ementorhub

Skills you'll gain

Statistical Methods

Classification And Regression Tree (CART)

Supervised Learning

Decision Tree Learning

Pandas (Python Package)

Dimensionality Reduction

Scikit Learn (Machine Learning Library)

Tensorflow

Applied Machine Learning

Feature Engineering

Machine Learning Algorithms

Artificial Neural Networks

Deep Learning

Regression Analysis

Machine Learning

Unsupervised Learning

Random Forest Algorithm

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