Data Science with R: Machine Learning

at NYC Data Science Academy - Midtown

(45)
Course Details
Price:
$2,840.50 9 seats left
Start Date:

Sat, Oct 26, 10:00am - Dec 07, 5:00pm (5 sessions)

Next start dates (2)

Location:
Midtown, Manhattan
500 8th Ave Ste 905
Btwn W 35th & W 36th Streets
New York, New York 10018
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Important:
No class on Nov 9th& Nov 30th
Purchase Options
Description
Class Level: Intermediate
Age Requirements: 18 and older
Average Class Size: 15
Teacher: David Romoff

What you'll learn in this data science course:

This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you 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. After successfully completing of this course, you 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 programming
  • Able to munge, analyze, and visualize data in R
Syllabus:
Unit 1: Foundations of Statistics and Simple Linear Regression
  • Understand your data
  • Statistical inference
  • Introduction to machine learning
  • Simple linear regression
  • Diagnostics and transformations
  • The coefficient of determination
Unit 2: Multiple Linear Regression and Generalized Linear Model
  • Multiple linear regression
  • Assumptions and diagnostics
  • Extending model flexibility
  • Generalized linear models
  • Logistic regression
  • Maximum likelihood estimation
  • Model interpretation
  • Assessing model fit
Unit 3: kNN and Naive Bayes, the Curse of Dimensionality
  • The K-Nearest Neighbors Algorithm
  • The choice of K and distance measure
  • Conditional probability: Bayes’ Theorem
  • The Naive Bayes’ Algorithm
  • The Laplace estimator
  • Dimension reduction
  • The PCA procedure
  • Ridge and Lasso regression
  • Cross-validation
Unit 4: Tree Models and SVMs
  • Decision trees
  • Bagging
  • Random forests
  • Boosting
  • Variable Importance
  • Hyperplanes and maximal margin classifier
  • Sort margin and support vector classifier
  • Kernels and support vector machines
Unit 5: Cluster Analysis and Neural Networks
  • Cluster analysis
  • K-means clustering
  • Hierarchical clustering
  • Neural networks and perceptrons
  • Sigmoid neurons
  • Network topology and hidden features
  • Back propagation learning with gradient descent
School Notes: We offer a certification licensed by the NYS Board of Education.

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Refund Policy
We offer full refund if you are not happy with the first class and decide to drop it.

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NYC Data Science Academy

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Start Dates (3)
Start Date Time Teacher # Sessions Price
10:00am - 5:00pm David Romoff 5 $2,840.50
This course consists of multiple sessions, view schedule for sessions.
Sat, Nov 02 10:00am - 5:00pm David Romoff
Sat, Nov 16 10:00am - 5:00pm David Romoff
Sat, Nov 23 10:00am - 5:00pm David Romoff
Sat, Dec 07 10:00am - 5:00pm David Romoff
10:00am - 5:00pm David Romoff 5 $2,990
This course consists of multiple sessions, view schedule for sessions.
Sat, Nov 02 10:00am - 5:00pm David Romoff
Sat, Nov 16 10:00am - 5:00pm David Romoff
Sat, Nov 23 10:00am - 5:00pm David Romoff
Sat, Dec 07 10:00am - 5:00pm David Romoff
10:00am - 5:00pm David Romoff 5 $2,840.50
This course consists of multiple sessions, view schedule for sessions.
Sat, Jan 25 10:00am - 5:00pm David Romoff
Sat, Feb 01 10:00am - 5:00pm David Romoff
Sat, Feb 08 10:00am - 5:00pm David Romoff
Sat, Feb 15 10:00am - 5:00pm David Romoff

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