1 Introduction

2 Machine Learning Pipeline

3 Pipeline MCQ

4 Look at the big picture

5 MCQ Big Picture

6 Objective and Benefits

7 Current Solution

8 Choosing solution MCQ

9 Frame The Problem

10 Supervised Learning

11 Create a music app

12 Movie recommendation- Supervised or not

13 Unsupervised Learning

14 Movie recommendation- Unsupervised or not

15 Suggest songs

16 Reinforcement Learning

17 Bike Rental Forecasting

18 Facial recognition

19 Facial Recognition-2

20 Facial Recognition- 3

21 Learning Type

22 Sub-domains of supervised learning

23 quiz classification or regression

24 Performance Measure

25 Check the Assumptions

26 Virtual Environment

27 Installing Libraries

28 Get the Data

29 Explore the Dataset

30 Quick description of the dataset

31 Total Instances

32 Missing Values

33 Non-Numeric Values

34 Identifying categorical attributes

35 Categories

36 Statistical description

37 Understanding statistical description

38 Standard Deviation

39 Range

40 Plotting histogram

41 Magic functions

42 Importing pyplot

43 Histograms

44 Conclusions from Histogram

45 Create a test set

46 Stratified sampling

47 Split criteria

48 StratifiedShuffleSplit

49 Create an object of StratifiedShuffleSplit

50 Generating test set- Splitting

51 Generating Test Set-Indices

52 Generating test set- Locating data

53 Conclusion

54 Explore the Data to gain Insights

55 copy() method

56 Visualizing geographical data

57 Visualizing the target variable

58 Insights from the plot

59 Looking for correlations

60 Computing correlation matrix

61 Correlation Matrix - 1

62 Correlation Matrix -2

63 Correlation matrix - 3

64 Scatter Matrix

65 Attribute with the most correlation

66 Attribute combinations

67 New Dataset

68 Prepare the Data for Machine Learning Algorithms

69 Missing values

70 Missing values -1

71 SimpleImputer

72 Handling categorical and text attributes

73 One Hot Encoding categories

74 Custom Transformers

75 Creating Custom Transformers

76 Feature Scaling

77 StandardScaler

78 Transformation Pipelines

79 ColumnTransformer

80 Exploring models

81 LinearRegression

82 Measuring error

83 Training a powerful model

84 Overfitting

85 Cross Validation

86 Performing cross Validation

87 Evaluating RandomForestRegressor

88 Observing Random Forests

89 Fine tune the model

90 GridSearchCV

91 Result of grid search

92 Evaluating the model on the test set

93 Analyzing model

94 Present the solution

95 Launch, monitor and maintain the system