1. PYTHON INTRODUCTION.pdf
10. ACCESSING METHODS OF CLASS.pdf
11. FOUR PILLARS OF OOPS CONCEPT.pdf
12. CREATING PYTHON CODE IN HTML.pdf
15. NUMPY RANDOM NUMBERS GENERATION.pdf
16. NUMPY BROADCASTING & PANDAS INTRODUCTION.pdf
18. WORKING WITH .CSV.pdf
2. BASICS OF PYTHON, IDENTIFIERS, RESERVED WORDS, DATATYPES.pdf
20. MERGING & GROUPBY.pdf
21. PLOTTING WITH PANDAS.pdf
22. UNI & BIVARIATE ANALYSIS.pdf
23. PLOTTING WITH SEABORN.pdf
25. REGULAR EXPRESSIONS.pdf
26. EXAMPLES OF REGEX.pdf
30. UNIFORM RANDOM VARIABLE.pdf
31. NORMAL DISTRIBUTION & ITS PROPERTIES, DATA ANALYTICS FRAMEWORKS.pdf
32. Q-Q PLOT, PARETO DISTRIBUTION, BOX-COX PLOT, PLOTTING UNIFORM & NORMAL DISTRIBUTION.pdf
33. MISSING VALUES TREATMENT.pdf
34. INFERENTIAL STATISTICS.pdf
35. STEPS FOR RANDOM SAMPLING WITH AN EXAMPLE.pdf
37. HYPOTHESIS TESTING WITH AN EXAMPLE.pdf
38. EXAMPLES OF HYPOTHESIS TESTING.pdf
40. MACHINE LEARNING & LINEAR ALGEBRA INTRODUCTION .pdf
42. LINEAR REGRESSION.pdf
43. GRADIENT DESCENT & OPTIMIZATION OF LINEAR REGRESSION.pdf
44. STEPS OF ALGORITHMS & EVALUATION METRICES.pdf
45. ASSUMPTIONS OF LINEAR REGRESSION.pdf
46. ADVANTAGES & DISADVANTAGES OF LINEAR REGRESSION, LOGISTIC REGRESSION.pdf
47. HANDLING UNDERFITTING & OVERFITTING, MISCLASSIFICATION & MAXIMUM LIKELIHOOD ESTIMATION.pdf
48. LOGISTIC REGRESSION ASSUMPTIONS, KNN ALGORITHM, HYPERPARAMETER TUNING.pdf
5. PROPERTIES OF DATATYPES.pdf
51. GINI INDEX, RANDOM FOREST, BAGGING.pdf
52. SUPPORT VECTOR MACHINE.pdf
53. PERFORMANCE METRICES.pdf
54. IMPORTANT CONCEPTS IN MACHINE LEARNING.pdf
55. GRID & RANDOMIZED SEARCH CV.pdf
56. NATURAL LANGUAGE PROCESSING.pdf
57. COMPONENTS OF NLP.pdf
58. K-MEANS CLUSTERING.pdf
59. HIERARCHICAL CLUSTERING, K-MEANS++, PCA.pdf
6. DECISION CONTROL STATEMENTS.pdf
60. VERSION CONTROL SYSTEM.pdf
9.2 CLASS & LOCAL, GLOBAL VARIABLES IN OOPS CONCEPT.pdf
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