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 Unsupervised Learning

13 Movie recommendation- Unsupervised or not

14 Suggest songs

15 Reinforcement Learning

16 Bike Rental Forecasting

17 Facial recognition

18 Facial Recognition-2

19 Facial Recognition- 3

20 Learning Type

21 Sub-domains of supervised learning

22 quiz classification or regression

23 Performance Measure

24 Check the Assumptions

25 Virtual Environment

26 Installing Libraries

27 Get the Data

28 Explore the Dataset

29 Quick description of the dataset

30 Total Instances

31 Missing Values

32 Non-Numeric Values

33 Identifying categorical attributes

34 Categories

35 Statistical description

36 Understanding statistical description

37 Standard Deviation

38 Range

39 Plotting histogram

40 Magic functions

41 Importing pyplot

42 Histograms

43 Conclusions from Histogram

44 Create a test set

45 Stratified sampling

46 Split criteria

47 StratifiedShuffleSplit

48 Create an object of StratifiedShuffleSplit

49 Generating test set- Splitting

50 Generating Test Set-Indices

51 Generating test set- Locating data

52 Conclusion

53 Explore the Data to gain Insights

54 copy() method

55 Visualizing geographical data

56 Visualizing the target variable

57 Insights from the plot

58 Looking for correlations

59 Computing correlation matrix

60 Correlation Matrix - 1

61 Correlation Matrix -2

62 Correlation matrix - 3

63 Scatter Matrix

64 Attribute with the most correlation

65 Attribute combinations

66 New Dataset

67 Prepare the Data for Machine Learning Algorithms

68 Missing values

69 Missing values -1

70 SimpleImputer

71 Handling categorical and text attributes

72 One Hot Encoding categories

73 Custom Transformers

74 Creating Custom Transformers

75 Feature Scaling

76 StandardScaler

77 Transformation Pipelines

78 ColumnTransformer

79 Exploring models

80 LinearRegression

81 Measuring error

82 Training a powerful model

83 Overfitting

84 Cross Validation

85 Performing cross Validation

86 Evaluating RandomForestRegressor

87 Observing Random Forests

88 Fine tune the model

89 GridSearchCV

90 Result of grid search

91 Evaluating the model on the test set

92 Analyzing model

93 Present the solution

94 Launch, monitor and maintain the system