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Data-Science-Machine-Learning-CodingNinjas

Phase 3 of Ninja Data Scientist Career Track.

  1. Introduction To Data Science
  2. Introduction to Python
  3. Conditions and Loops
  4. Patterns
  5. More on Loops
  6. Strings, List & 2D List
  7. Functions
  8. Tuples, Dictionary And Sets
  9. Object Oriented Programming Systems(OOPs)
  10. Working With Files
  11. NumPy
  12. Pandas
  13. Plotting Graphs
  14. Structured Query Language(SQL) - Basic
  15. Structured Query Language(SQL) - Advance
  16. Indexing And SQLite
  17. Application Programming Interfaces(APIs) - I
  18. Application Programming Interfaces(APIs) - II
  19. Web Scraping - BeautifulSoup
  20. Web Scraping - Selenium
  21. Web Scraping - Advanced Selenium
  22. Statistics
  23. Descriptive Statistics
  24. Introduction to Inferential Statistics
  25. Inferential Statistics : Hypothesis Testing
  26. Introduction to Machine Learning
  27. Linear Regression
  28. MultiVariable Regression And Gradient Descent
  29. Feature Scaling
  30. Logistic Regression
  31. Classification Measures
  32. Decision Trees - I
  33. Decision Trees - II
  34. Random Forests
  35. Naive Bayes
  36. K-Nearest Neighbours(K-NN)
  37. Support Vector Machine(SVM)
  38. Principal Component Analysis(PCA) - I
  39. Principal Component Analysis(PCA) - II
  40. Natural Language Processing(NLP)
  41. Neural Networks
  42. Tensor Flow
  43. Keras
  44. Convolutional Neural Network(CNN) - I
  45. Convolutional Neural Network(CNN) - II
  46. Recurrent Neural Network(RNN)
  47. Long Short-Term Memory(LSTM)
  48. Unsupervised Learning - I
  49. Unsupervised Learning - II
  50. Introduction to Data Visualization
  51. Getting Familiar with Tableau
  52. Tableau Visualizations
  53. Seaborn
  54. Git

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Phase 3 of Ninja Data Scientist Career Track.

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