Data Science in Python: Regression & Forecasting

سرفصل های دوره

Learn Python for Data Science & Machine Learning, and build regression and forecasting models with hands-on projects

1 - Getting Started

1 - Course Introduction

2 - About This Series

3 - Course Structure Outline

4 - READ ME Important Notes for New Students.html

5 - DOWNLOAD Course Resources.html

5 - Data-Science-in-Python-Regression.pdf

5 - Data-Science-in-Python-Regression.zip

6 - Introducing the Course Project

7 - Setting Expectations

8 - Jupyter Installation Launch

2 - Intro to Data Science

9 - What is Data Science

10 - Data Science Skillset

11 - What is Machine Learning

12 - Common Machine Learning Algorithms

13 - Data Science Workflow

14 - Step 1 Scoping a Project

15 - Step 2 Gathering Data

16 - Step 3 Cleaning Data

17 - Step 4 Exploring Data

18 - Step 5 Modeling Data

19 - Step 6 Sharing Insights

20 - Regression Modeling

21 - Key Takeaways

3 - Regression 101

22 - Regression 101

23 - Goals of Regression

24 - Types of Regression

25 - Regression Modeling Workflow

26 - Key Takeaways

4 - PreModeling Data Prep EDA

27 - EDA for Regression

28 - Exploring the Target

29 - Exploring the Features

30 - ASSIGNMENT Exploring the Target Features

31 - SOLUTION Exploring the Target Features

32 - Linear Relationships Correlation

33 - Linear Relationships in Python

34 - FeatureTarget Relationships

35 - FeatureFeature Relationships

36 - PRO TIP Pairplots Lmplots

37 - ASSIGNMENT Exploring Relationships

38 - SOLUTION Exploring Relationships

39 - Preparing For Modeling

40 - Key Takeaways

5 - Simple Linear Regression

41 - Simple Linear Regression

42 - The Linear Regression Model

43 - Least Squared Error

44 - Linear Regression in Python

45 - Linear Regression in Statsmodels

46 - Interpreting the Model

47 - Making Predictions

48 - RSquared

49 - Hypothesis Tests

50 - The FTest

51 - Coefficient Estimates PValues

52 - Residual Plots

53 - CASE STUDY Modeling Health Insurance Prices

54 - ASSIGNMENT Simple Linear Regression

55 - SOLUTION Simple Linear Regression

56 - Key Takeaways

6 - Multiple Linear Regression

57 - Multiple Linear Regression Equation

58 - Fitting a Multiple Linear Regression

59 - Interpreting Multiple Linear Regression Models

60 - Variable Selection

61 - ASSIGNMENT Multiple Linear Regression

62 - SOLUTION Multiple Linear Regression

63 - Mean Error Metrics

64 - DEMO Mean Error Metrics

65 - Adjusted RSquared

66 - ASSIGNMENT Mean Error Metrics

67 - SOLUTION Mean Error Metrics

68 - Key Takeaways

7 - Model Assumptions

69 - Assumptions of Linear Regression

70 - Linearity

71 - Independence of Errors

72 - Normality of Errors

73 - DEMO Normality of Errors

74 - PRO TIP Interpreting Transformed Targets

75 - No Perfect Multicollinearity

76 - Equal Variance of Errors

77 - Outliers Leverage Influence

78 - RECAP Assumptions of Linear Regression

79 - ASSIGNMENT Model Assumptions

80 - SOLUTION Model Assumptions

81 - Key Takeaways

8 - Model Testing Validation

82 - Model Scoring Steps

83 - Data Splitting

84 - Overfitting Underfitting

85 - The BiasVariance Tradeoff

86 - Validation Data

87 - Model Tuning

88 - Model Scoring

89 - Cross Validation

90 - Simple vs Cross Validation

91 - ASSIGNMENT Model Testing Validation

92 - SOLUTION Model Testing Validation

93 - Key Takeaways

9 - Feature Engineering

94 - Intro To Feature Engineering

95 - Feature Engineering Techniques

96 - Polynomial Terms

97 - Combining Features

98 - Interaction Terms

99 - Categorical Features

100 - Dummy Variables

101 - DEMO Dummy Variables

102 - Binning Categorical Data

103 - Binning Numeric Data

104 - DEMO Additional Feature Engineering Ideas

105 - ASSIGNMENT Feature Engineering

106 - SOLUTION Feature Engineering

107 - Key Takeaways

10 - Project 1 San Francisco Rent Prices

108 - Project Brief

109 - Solution Walkthrough

11 - Regularized Regression

110 - Intro to Regularized Regression

111 - Ridge Regression

112 - Standardization

113 - Fitting a Ridge Regression Model

114 - DEMO Fitting a Ridge Regression

115 - PRO TIP RidgeCV

116 - ASSIGNMENT Ridge Regression

117 - SOLUTION Ridge Regression

118 - Lasso Regression

119 - PRO TIP LassoCV

120 - ASSIGNMENT Lasso Regression

121 - SOLUTION Lasso Regression

122 - Elastic Net Regression

123 - DEMO Fitting an Elastic Net Regression

124 - PRO TIP ElasticNetCV

125 - ASSIGNMENT Elastic Net Regression

126 - SOLUTION Elastic Net Regression

127 - RECAP Regularized Regression Models

128 - PREVIEW Tree Based Models

129 - Key Takeaways

12 - Project 1 San Francisco Rent Prices Continued

130 - Project Brief

131 - Solution Walkthrough

13 - Time Series Analysis

132 - Intro to Time Series

133 - Moving Averages

134 - DEMO Moving Averages

135 - Exponential Smoothing

136 - ASSIGNMENT Smoothing

137 - SOLUTION Smoothing

138 - Decomposition

139 - DEMO Decomposition

140 - PRO TIP Autocorrelation Chart

141 - ASSIGNMENT Decomposition

142 - SOLUTION Decomposition

143 - Forecasting

144 - Linear Regression With Trend Season

145 - DEMO Linear Regression With Trend Season

146 - Facebook Prophet

147 - ASSIGNMENT Forecasting

148 - SOLUTION Forecasting

149 - Key Takeaways

14 - Project 2 Electricity Consumption

150 - Project Brief

151 - Solution Walkthrough

15 - Next Steps

152 - EXTRA LESSON.html

45,900 تومان

خرید اشتراک افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: Udemy