R Machine Learning

This course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques in R.

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Levels
Intermediate
Video Hours
22:00:33
Exercises
143
Videos
16

Course Introduction

Course Overview

This course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques in R. An understanding of underlying algorithms is important to understand the mechanism of programming machine learning techniques. It helps data scientists to detect problems and improve performance. After successful completion of this course, students will be able to break down the mathematics behind major machine learning algorithms and explain the principles of machine learning algorithms. This course will introduce students to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis and neural networks. To pass this unit students are required to complete their homework.

Course Goal

Students will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.

Prerequisites

  • Knowledge of R.
  • Able to munge, analyze, and visualize data in R

Instructors

David Romoff

David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. Previously, David worked in Enterprise Risk Management at AIG for five years were he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.

Aiko Liu

Aiko grew up in Taiwan where he studied Mathematics and Physics in college. He then moved to the United States to obtain his PhD in Mathematics at Harvard. After finishing his degree, Aiko conducted research and taught at M.I.T and U.C Berkeley for nine years before moving into the world of finance. He worked in the hedge fund industry on quantitative trading for a decade before diving into Data Science full time. Aiko enjoys programming and using machine learning algorithms for industrial research. When at home he enjoys reading books on a really, really wide variety of topics

Curriculum

Foundations of Statistics
Missingness, Imputation, & kNN
Simple Linear Regression
Multiple Linear Regression
Generalized Linear Models
Principal Component Analysis
Regularization & Cross Validation
Cluster Analysis
Trees, Bagging, Random Forests, and Boosting
Support Vector Machines
Association Rules
Naive Bayes
Time Series Analysis
Neural Networks
Introduction to A/B Testing

Introduction to XGBoost (Advanced Content)

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R Machine Learning

$2990.0

This course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications of machine learning techniques in R.

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