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