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Course: Machine Learning using R and Python

Machine Learning using R and Python

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course
  • This course has been prepared for professionals aspiring to learn the basics of R and Python and develop applications involving machine learning techniques such as recommendation, classification, regression and clustering
  • Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language like R and Python and its packages
  • After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques

Who this course is for:

  • All graduates or pursuing students
Basic knowledge
  • Before you start proceeding with this course, we assume that you have a prior exposure to R packages and Python, Numpy, pandas, scipy, matplotlib, Windows and any of the Linux operating system flavors. If you are new to any of these concepts, here you can learn all the concepts from basics on wards
What you will learn
  • This course has been prepared for professionals aspiring to learn the basics of R and Python to develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language R and Python with its packages. After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques
Curriculum
Number of Lectures: 83
Total Duration: 69:42:18
MACHINE LEARNING using R and PYTHON
  • 1. Introduction to Machine Learning  
  • 2. Introduction to R Programming  
  • 3. R Installation & Setting R Environment  
  • 4. Variables, Operators & Data types  
  • 5. Structures  
  • 6. Vectors  
  • 7. Vector Manipulation & Sub-Setting  
  • 8. Constants  
  • 9. RStudio Installation & Lists Part 1  
  • 10. Lists Part 2  
  • 11. List Manipulation, Sub-Setting & Merging  
  • 12. List to Vector & Matrix Part 1  
  • 13. Matrix Part 2  
  • 14. Matrix Accessing  
  • 15. Matrix Manipulation, rep fn & Data Frame  
  • 16. Data Frame Accessing  
  • 17. Column Bind & Row Bind  
  • 18. Merging Data Frames Part 1  
  • 19. Merging Data Frames Part 2  
  • 20. Melting & Casting  
  • 21. Arrays  
  • 22. Factors  
  • 23. Functions & Control Flow Statements  
  • 24. Strings & String Manipulation with Base Package  
  • 25. String Manipulation with Stringi Package Part 1  
  • 26. String Manipulation with Stringi Package Part 2 & Date and Time Part 1  
  • 27. Date and Time Part 2  
  • 28. Data Extraction from CSV File  
  • 29. Data Extraction from EXCEL File  
  • 30. Data Extraction from CLIPBOARD, URL, XML & JSON Files  
  • 31. Introduction to DBMS  
  • 32. Structured Query Language  
  • 33. Data Definition Language Commands  
  • 34. Data Manipulation Language Commands  
  • 35. Sub Queries & Constraints  
  • 36. Aggregate Functions, Clauses & Views  
  • 37. Data Extraction from Databases Part 1  
  • 38. Data Extraction from Databases Part 2 & DPlyr Package Part 1  
  • 39. DPlyr Package Part 2  
  • 40. DPlyr Functions on Air Quality Data Set  
  • 41. Plyr Package for Data Analysis  
  • 42. Tidyr Package with Functions  
  • 43. Factor Analysis  
  • 44. Prob.Table & CrossTable  
  • 45. Statistical Observations Part 1  
  • 46. Statistical Observations Part 2  
  • 47. Statistical Analysis on Credit Data set  
  • 48. Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts  
  • 49. Box Plots  
  • 50. Histograms & Line Graphs  
  • 51. Scatter Plots & Scatter plot Matrices  
  • 52. Low Level Plotting  
  • 53. Bar Plot & Density Plot  
  • 54. Combining Plots  
  • 55. Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot  
  • 56. MatPlot, ECDF & BoxPlot with IRIS Data set  
  • 57. Additional Box Plot Style Parameters  
  • 58. Set.Seed Function & Preparing Data for Plotting  
  • 59. QPlot, ViolinPlot, Statistical Methods & Correlation Analysis  
  • 60. ChiSquared Test, T Test, ANOVA  
  • 61. Data Exploration and Visualization  
  • 62. Machine Learning, Types of ML with Algorithms  
  • 63. How Machine Solve Real Time Problems  
  • 64. K-Nearest Neighbor(KNN) Classification  
  • 65. KNN Classification with Cancer Data set Part 1  
  • 66. KNN Classification with Cancer Data set Part 2  
  • 67. Navie Bayes Classification  
  • 68. Navie Bayes Classification with SMS Spam Data set & Text Mining  
  • 69. WordCloud & Document Term Matrix  
  • 70. Train & Evaluate a Model using Navie Bayes  
  • 71. MarkDown using Knitr Package  
  • 72. Decision Trees  
  • 73. Decision Trees with Credit Data set Part 1  
  • 74. Decision Trees with Credit Data set Part 2  
  • 75. Support Vector Machine, Neural Networks & Random Forest  
  • 76. Regression & Linear Regression  
  • 77. Multiple Regression  
  • 78. Generalized Linear Regression, Non Linear Regression & Logistic Regression  
  • 79. Clustering  
  • 80. K-Means Clustering with SNS Data Analysis  
  • 81. Association Rules (Market Basket Analysis)  
  • 82. Market Basket Analysis using Association Rules with Groceries Dataset  
  • 83. Python Libraries for Data Science  
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