Certificate in Machine Learning with R

Machine Learning with R

About Machine Learning with R

Computer science's machine learning field examines how to create algorithms that can learn. Concept learning, function learning, or "predictive modeling," grouping, and identifying predictive patterns are typical machine learning tasks.

Another programming language that has grown significantly in popularity during the past ten years is R. Today, it is utilized for data science and machine learning after being first developed for statistical computing.

Why is Machine Learning with R important?

As it makes it simple for researchers to mix many machine learning algorithms into a single program, the R language has gained popularity. Additionally, it offers an easy method for researchers to exchange codes. R Programming:

• Gets you High Paying Jobs.
• Best for Statistical Analysis and Data Science.
• Is used by Top Companies.
• Is used to Create Interactive Web-Apps and Stunning Visualizations.
• Provides a Comprehensive Library.
• Has a Huge Community.

Who should take the Machine Learning with R Exam?

• Students
• Working Professionals
• Statisticians

Machine Learning with R Certification Course Outline

1. Basics of R and R studio
2. Packages in R
3. Inputting data
4. Creating Barplots in R
5. Creating Histograms in R
6. Types of Data
7. Types of Statistics
8. Describing the data graphically
9. Measures of Centers
10. Measures of Dispersion
11. Introduction to Machine Learning
12. Building a Machine Learning Model
13. Data Exploration
14. The Data and the Data Dictionary
15. Importing the dataset into R
16. Univariate Analysis and EDD
17. Missing Value imputation
18. Bi-variate Analysis and Variable Transformation
19. Variable transformation in R
20. Non Usable Variables
21. Dummy variable creation in R
22. Correlation Matrix and cause-effect relationship
23. Basic equations and Ordinary Least Squared (OLS) method
24. Assessing Model Accuracy - RSE and R squared
25. Simple Linear Regression in R
26. Multiple Linear Regression
27. Interpreting results for categorical Variable
28. Multiple Linear Regression in R
29. Bias Variance trade-off
30. Test-Train Split in R
31. Ridge regression and Lasso in R
32. Importing the data into R
33. Logistic Regression
34. Results of Simple Logistic Regression
35. Logistics with multiple predictors
36. Confusion Matrix
37. Evaluating Model performance
38. Linear Discriminant Analysis
39. Understanding the results of classification models
40. Introduction to Decision trees
41. Basics of Decision Trees
42. Understanding a Regression Tree
43. Importing the Data set into R
44. Building a Regression Tree in R
45. Boosting techniques
46. Gradient Boosting in R
47. AdaBoosting in R
48. XGBoosting in R
49. Introduction to SVM
50. The Concept of a Hyperplane
51. Maximum Margin Classifier
52. Limitations of Maximum Margin Classifier
53. Support Vector classifiers
54. Limitations of Support Vector Classifiers
55. Kernel Based Support Vector Machines
56. Importing and preprocessing data
57. Classification SVM model using Linear Kernel
58. Hyperparameter Tuning for Linear Kernel
59. Polynomial Kernel with Hyperparameter Tuning
60. Radial Kernel with Hyperparameter Tuning
61. SVM based Regression Model in R