Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
1. PYTHON INTRODUCTION.pdf		1. PYTHON INTRODUCTION.pdf
10. ACCESSING METHODS OF CLASS.pdf		10. ACCESSING METHODS OF CLASS.pdf
11. FOUR PILLARS OF OOPS CONCEPT.pdf		11. FOUR PILLARS OF OOPS CONCEPT.pdf
12. CREATING PYTHON CODE IN HTML.pdf		12. CREATING PYTHON CODE IN HTML.pdf
13. JINJA TEMPLATING.pdf		13. JINJA TEMPLATING.pdf
14. NUMPY.pdf		14. NUMPY.pdf
15. NUMPY RANDOM NUMBERS GENERATION.pdf		15. NUMPY RANDOM NUMBERS GENERATION.pdf
16. NUMPY BROADCASTING & PANDAS INTRODUCTION.pdf		16. NUMPY BROADCASTING & PANDAS INTRODUCTION.pdf
17. PANDAS.pdf		17. PANDAS.pdf
18. WORKING WITH .CSV.pdf		18. WORKING WITH .CSV.pdf
19. PANDAS DATAFRAME.pdf		19. PANDAS DATAFRAME.pdf
2. BASICS OF PYTHON, IDENTIFIERS, RESERVED WORDS, DATATYPES.pdf		2. BASICS OF PYTHON, IDENTIFIERS, RESERVED WORDS, DATATYPES.pdf
20. MERGING & GROUPBY.pdf		20. MERGING & GROUPBY.pdf
21. PLOTTING WITH PANDAS.pdf		21. PLOTTING WITH PANDAS.pdf
22. UNI & BIVARIATE ANALYSIS.pdf		22. UNI & BIVARIATE ANALYSIS.pdf
23. PLOTTING WITH SEABORN.pdf		23. PLOTTING WITH SEABORN.pdf
24. PLOTLY.pdf		24. PLOTLY.pdf
25. REGULAR EXPRESSIONS.pdf		25. REGULAR EXPRESSIONS.pdf
26. EXAMPLES OF REGEX.pdf		26. EXAMPLES OF REGEX.pdf
27. WEB SCRAPING.pdf		27. WEB SCRAPING.pdf
28. PROBABILITY.pdf		28. PROBABILITY.pdf
29. RANDOM VARIABLE.pdf		29. RANDOM VARIABLE.pdf
3. OPERATORS.pdf		3. OPERATORS.pdf
30. UNIFORM RANDOM VARIABLE.pdf		30. UNIFORM RANDOM VARIABLE.pdf
31. NORMAL DISTRIBUTION & ITS PROPERTIES, DATA ANALYTICS FRAMEWORKS.pdf		31. NORMAL DISTRIBUTION & ITS PROPERTIES, DATA ANALYTICS FRAMEWORKS.pdf
32. Q-Q PLOT, PARETO DISTRIBUTION, BOX-COX PLOT, PLOTTING UNIFORM & NORMAL DISTRIBUTION.pdf		32. Q-Q PLOT, PARETO DISTRIBUTION, BOX-COX PLOT, PLOTTING UNIFORM & NORMAL DISTRIBUTION.pdf
33. MISSING VALUES TREATMENT.pdf		33. MISSING VALUES TREATMENT.pdf
34. INFERENTIAL STATISTICS.pdf		34. INFERENTIAL STATISTICS.pdf
35. STEPS FOR RANDOM SAMPLING WITH AN EXAMPLE.pdf		35. STEPS FOR RANDOM SAMPLING WITH AN EXAMPLE.pdf
36. USING T-SCORE.pdf		36. USING T-SCORE.pdf
37. HYPOTHESIS TESTING WITH AN EXAMPLE.pdf		37. HYPOTHESIS TESTING WITH AN EXAMPLE.pdf
38. EXAMPLES OF HYPOTHESIS TESTING.pdf		38. EXAMPLES OF HYPOTHESIS TESTING.pdf
39. CHI-SQUARE TEST.pdf		39. CHI-SQUARE TEST.pdf
4. DATATYPES .pdf		4. DATATYPES .pdf
40. MACHINE LEARNING & LINEAR ALGEBRA INTRODUCTION .pdf		40. MACHINE LEARNING & LINEAR ALGEBRA INTRODUCTION .pdf
41. LINEAR ALGEBRA.pdf		41. LINEAR ALGEBRA.pdf
42. LINEAR REGRESSION.pdf		42. LINEAR REGRESSION.pdf
43. GRADIENT DESCENT & OPTIMIZATION OF LINEAR REGRESSION.pdf		43. GRADIENT DESCENT & OPTIMIZATION OF LINEAR REGRESSION.pdf
44. STEPS OF ALGORITHMS & EVALUATION METRICES.pdf		44. STEPS OF ALGORITHMS & EVALUATION METRICES.pdf
45. ASSUMPTIONS OF LINEAR REGRESSION.pdf		45. ASSUMPTIONS OF LINEAR REGRESSION.pdf
46. ADVANTAGES & DISADVANTAGES OF LINEAR REGRESSION, LOGISTIC REGRESSION.pdf		46. ADVANTAGES & DISADVANTAGES OF LINEAR REGRESSION, LOGISTIC REGRESSION.pdf
47. HANDLING UNDERFITTING & OVERFITTING, MISCLASSIFICATION & MAXIMUM LIKELIHOOD ESTIMATION.pdf		47. HANDLING UNDERFITTING & OVERFITTING, MISCLASSIFICATION & MAXIMUM LIKELIHOOD ESTIMATION.pdf
48. LOGISTIC REGRESSION ASSUMPTIONS, KNN ALGORITHM, HYPERPARAMETER TUNING.pdf		48. LOGISTIC REGRESSION ASSUMPTIONS, KNN ALGORITHM, HYPERPARAMETER TUNING.pdf
49. KNN REGRESSION.pdf		49. KNN REGRESSION.pdf
5. PROPERTIES OF DATATYPES.pdf		5. PROPERTIES OF DATATYPES.pdf
50. DECISION TREE.pdf		50. DECISION TREE.pdf
51. GINI INDEX, RANDOM FOREST, BAGGING.pdf		51. GINI INDEX, RANDOM FOREST, BAGGING.pdf
52. SUPPORT VECTOR MACHINE.pdf		52. SUPPORT VECTOR MACHINE.pdf
53. PERFORMANCE METRICES.pdf		53. PERFORMANCE METRICES.pdf
54. IMPORTANT CONCEPTS IN MACHINE LEARNING.pdf		54. IMPORTANT CONCEPTS IN MACHINE LEARNING.pdf
55. GRID & RANDOMIZED SEARCH CV.pdf		55. GRID & RANDOMIZED SEARCH CV.pdf
56. NATURAL LANGUAGE PROCESSING.pdf		56. NATURAL LANGUAGE PROCESSING.pdf
57. COMPONENTS OF NLP.pdf		57. COMPONENTS OF NLP.pdf
58. K-MEANS CLUSTERING.pdf		58. K-MEANS CLUSTERING.pdf
59. HIERARCHICAL CLUSTERING, K-MEANS++, PCA.pdf		59. HIERARCHICAL CLUSTERING, K-MEANS++, PCA.pdf
6. DECISION CONTROL STATEMENTS.pdf		6. DECISION CONTROL STATEMENTS.pdf
60. VERSION CONTROL SYSTEM.pdf		60. VERSION CONTROL SYSTEM.pdf
7. FUNCTIONS.pdf		7. FUNCTIONS.pdf
8. OOPS CONCEPT.pdf		8. OOPS CONCEPT.pdf
9.1 HTML.pdf		9.1 HTML.pdf
9.2 CLASS & LOCAL, GLOBAL VARIABLES IN OOPS CONCEPT.pdf		9.2 CLASS & LOCAL, GLOBAL VARIABLES IN OOPS CONCEPT.pdf
README.md		README.md

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DATA-SCIENCE-NOTES

PYTHON, MACHINE LEARNING, SQL, TABLEAU

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DATA-SCIENCE-NOTES

I'm sharing the notes which is writen on my own by taking some industrial experts help.

Hope this is very helpful to the Data Science Community People.

The notes written is very simple and easy to understand and implement too.

Thank you so much for refering my notes.

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