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Data Science with R: Machine Learning

  • Intermediate
  • 18 and older
  • $2,990
  • Virtual Classroom
  • 35 hours over 5 sessions

Start Dates (0)

  • $2,990
  • Virtual Classroom
  • 35 hours over 5 sessions
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Class Description

Description

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.

Refund Policy

Any student wishing to withdraw from a program must notify CourseHorse in writing. The date of withdrawal for refund purposes is the last date of physical attendance. The failure of a student to notify us in writing of withdrawal may delay refund of tuition due pursuant to Section 5001 and 5002 of the Education Law.

Any student requesting cancellation within seven days after signing the Enrollment Agreement but before instruction begins will be refunded all money paid less 5% cancellation fee. Thereafter, in the event of cancellation or termination by the school, refunds will be prorated based on the student's last date of attendance.

Reviews of Classes at NYC Data Science Academy (31)

(4.4-star rating across 31 reviews)
  • Data Science with R: Machine Learning

    Anonymous review on 12/9/2019
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Review Summary by CourseHorse

The students who took the Data Science with R: Machine Learning course found it to be intense but extremely valuable. They praised the instructor, Shu Yan, for his ability to explain complicated statistical concepts and translate them into R coding techniques. The course materials, in-class practices, and homework assignments were all helpful for learning and future reference. Many students felt that the course was a good starting point for those interested in data science and machine learning, as it improved their understanding and coding skills in R. The classes were well-structured and covered a wide range of practical techniques that could be applied to real-world work. Additionally, the opportunity to network with classmates who had industry experience was seen as a valuable aspect of the course. Overall, students were satisfied with what they learned and would recommend the course to others. "I would recommend this course to anyone who is interested in data science/machine learning but doesn't know much about this field." "I took Machine Learning with R and Hadoop data engineering classes in 2015. They are all well-structured classes with extensive information coverage and concrete learning process design. All the techniques told in the class are very practical and can be applied to work very fast." "Took the weekend course for Machine Learning with R. Course was very helpful in helping me understand the basics of Machine Learning and different models. My instructor was Luke. He was very helpful and would spend enough time covering each topic. He even took an additional class because he didn't want to rush through the material."

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NYC Data Science Academy

NYC Data Science Academy is a program designed to teach those who wish to learn.

Through hands-on projects and real-world applications, our students develop the skills they will need to pursue data science as both a hobby and profession. We also organize the NYC Open Data Meetup, which means that by...

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