Requirements

  • • Anyone in the world interested in Outstanding Opportunities and Growth in Artificial Intelligence, Machine Learning & Deep Learning Easily and Quickly
  • • Students who want to learn how to boost their career in Data Science
  • • Professionals who want to know it all on how to become a Data Scientist

Outcomes

  • • You will get truly adept at predictive modelling problems and solutions end-to-end.
  • • You can show this expertise by building up an AI and Machine learning portfolio of completed projects.
  • • You Learn precisely how to utilize open source tools and libraries to get solutions in machine learning projects.
  • • The Course shows you how to utilize a library to function through a task from end-to-end and deliver results
  • • The Course is deliberately intended for professionals and students who need to get results, quick.

Description

Python and Statistics for Data Science


1. Python Introduction

2. Python Practical Task

3. Numerical Python

4. Numerical Python Practical Session

5. Numerical Python Practical Task

6. Pandas Data Analysis Part 1

7. Pandas Data Analysis Part 2

8. Pandas Data Analysis Practical Task

9. Matplotlib Data Visualization Part 1

10. Matplotlib Data Visualization Part 2

11. Matplotlib Data Visualization Practical Session

12. Matplotlib Data Visualization Practical Task

13. Statistics for Data Science Part 1

14. Statistics for Data Science Part 2


Beginner’s - Artificial Intelligence, Machine Learning and Business Analytics


15. Business Analytics & Business Intelligence

16. Machine Learning & Artificial Intelligence

17. How to Work in the Cloud Practical Session

18. Machine Learning Techniques Part 1

19. Machine Learning Techniques Part 2

20. How to Clean Data Practical Session

21. Sports Analytics Project Session

22. Sports Analytics Project Task

23. HR Salary Analytics Project Session

24. HR Salary Analytics Project Task

25. Survival Analytics Project Session

26. Survival Analytics Project Task

27. Health Care Analytics Project Session

28. Health Care Analytics Project Task


Advanced - Artificial Intelligence and Machine Learning 


29. Decision Tree and Random Forest Algorithm

30. Plant Analytics Project Session Part 1

31. Plant Analytics Project Task Part 1

32. Naïve Bayes and KNN Algorithm

33. Support Vector Machine Algorithm

34. Plant Analytics Project Session Part 2

35. Plant Analytics Project Task Part 2

36. How to Calculate Feature Importance Practical Session

37. Ensemble Learning Techniques

38. Ensemble Learning Project Session

39. Ensemble Learning Project Task

40. Data Mining

41. Web Scraping using Python Beautiful Soup

42. Web Scraping using Beautiful Soup Practical Session

43. Web Scraping using Beautiful Soup Practical Task

44. Time Series Analysis 

45. Time Series Analysis Practical Session

46. Time Series Analysis Practical Task

47. Bank Loan (Credit Risk) Project Session

48. Bank Loan (Credit Risk) Project Task

49. Cyber Security Anomaly Detection Project Session

50. Cyber Security Anomaly Detection Project Task

51. eCommerce Sales Project Session

52. eCommerce Sales Project Task


Deep Learning and Computer Vision


53. Natural Language Processing (NLP) & Text Mining

54. Sentiment Analysis using Text Blob Practical Session

55. Sentiment Analysis using Text Blob Practical Task

56. Natural Language Processing using NLTK Practical Session

57. Natural Language Processing using NLTK Practical Task

58. Market Basket Analysis

59. Market Basket Analysis Practical Session

60. Market Basket Analysis Practical Task

61. Recommendation System Project Session

62. Recommendation System Project Task

Student feedback

4.6
(2,533)

Course Rating

  • 5 stars
    77 %
  • 4 stars
    54 %
  • 3 stars
    14 %
  • 2 stars
    5 %
  • 1 stars
    2 %

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