Machine Learning Specialist

It gives a brief idea of mathematical preliminaries, and gives an introduction to R programming language, which is popular language to do machine learning. Concepts of linear and logistic regression are introduced with examples. Then algorithms like Naive-Bayesian, kNN, decision trees, random forests, and support vector machines are introduced. Enough time is spent on understanding the concept behind each algorithm and examples and case studies are provided.

  • Training Type Online
  • Course Duration 6 weeks, 10 hrs/week
$ 800

Want to know more?

COURSES INCLUDED

  • Introduction to R
  • Data Processing using R
  • Programming in R
  • Graphics Using R
  • Machine Learning

Introduction to R

  1. What is R?
  2. Why R?
  3. Installing R
  4. R environment
  5. How to get help in R
  6. R console and Editor
  7. R Studio
  8. Variables in R
  9. Scalars
  10. Vectors
  11. Matrices
  12. List
  13. Data frames
  14. Using c, Cbind, Rbind, attach and detach functions in R
  15. Factors

Data Processing using R

  1. Reading Data
  2. Writing Data
  3. Slicing of Data
  4. Merging Data
  5. Apply functions

Programming in R

  1. If
  2. For
  3. While
  4. Repeat
  5. Break
  6. Return
  7. Functions
  8. Name matching of arguments

Graphics Using R

  1. Box plot
  2. Histogram
  3. Pareto charts
  4. Pie graph
  5. Line chart
  6. Scatterplot
  7. Developing graphs

Machine Learning

  1. Simulation (Sampling from Probability Distributions)
  2. Supervised and Unsupervised Learning
  3. Regression
  4. Prediction
  5. Forecasting
  6. Dimension reduction (Principal Component Analysis)
  7. Decision Tree
  8. Anomaly Detection in data
  9. Data clustering (k-means)
  10. Classification (k- nearest neighbor)
  11. Classification (Support Vector Machine)