Ementorhub

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