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Machine Learning Using R And Python




Python and Machine Learning Professional Course


Freshers and Career Changers


Regular Offline and Online Live Training


Week Days and Week Ends

Duration :

30 to 45 days

Python and Machine Learning What will you learn?

•An overview about Python and Machine Learning concepts.
•Understand how to navigate and use Python and Machine Learning.
•Learn from simple and interactive sessions on Python and Machine Learning
•How to perform read and write operations in Python and Machine Learning.
•Learning and Creating a complete Python and Machine Learning project in depth
•Learn Python and Machine Learning For Beginners. The Complete Course With Practical Examples
•Learn to code in Python and Machine Learning from scratch with hands-on projects
•Understand Python and Machine Learning and how to use it in designing and building apps.
•Dive in and learn Python and Machine Learning step-by-step from beginner to intermediate level by building a practical project!

machine learning using r and python Course Highlights

•We are Known for High-Quality Training
•Training by Industry expert professionals
•Accessibility of adequate training resources
•Create hands-on projects at the end of the course
•Highly Experienced Trainer with 10+ Years in MNC Company
• Finessing your tech skills and help break into the IT field
•Our trainers have experience in training End Users & Students & Corporate employees.
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Python and Machine Learning

•.net Developer, Business Analysis, Software Testing, Software Development, Linux Administration, java, Automation Testing, hybris, qtp, lamp, css, xml, manual
•HPSM, HPAM, HP PPM, HPBSM, Python, SAP Apo, SAP APO DP, SAP APO SNP, Testing, HP DMA, SAP MM, Mainframe Developer, ETL Testing, JAVA Developer
•Magento, Java, Adfs, Mule Esb, Dell Boomi, Backend Developer, Sap Bo, Sap Apo, .Net, L2 Deveoper, Python Developer, Quality Assurance Engineering, Etl
•python, django, aws, Data Analytics, Full Stack Developer, Front End, ui/ux design, Ui Development, User Interface Designing, Jquery, Javascript
•Web Application Developers, Java Developers, DBA LEAD, DBA Manager, Asset Control developer, embedded software engineer, oracle applications technical


•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
•9. RStudio Installation & Lists
•10. Lists
•12. List to Vector & Matrix
•13. Matrix
•18. Merging Data Frames
•19. Merging Data Frames
•25. String Manipulation with Stringi Package
•26. String Manipulation with Stringi Package & Date and Time
•27. Date and Time
•37. Data Extraction from Databases
•38. Data Extraction from Databases & DPlyr Package
•39. DPlyr Package
•45. Statistical Observations
•46. Statistical Observations
•65. KNN Classification with Cancer Data set
•66. KNN Classification with Cancer Data set
•73. Decision Trees with Credit Data set
•74. Decision Trees with Credit Data set