The Complete Visual Guide to Machine Learning & Data Science

سرفصل های دوره

Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)

1 - Getting Started

1 - Course Structure Outline

2 - READ ME Important Notes for New Students.html

3 - DOWNLOAD Course Resources.html

3 - Maven-ML-Demos.xlsx

3 - Maven-ML-Demos-COMPLETE.xlsx

3 - Visual-Guide-to-Machine-Learning.pdf

4 - Setting Expectations

2 - PART 1 QA Data Profiling

5 - Part 1 QA Data Profiling

3 - Intro to the ML Landscape

6 - Intro to Machine Learning

7 - When is ML the right fit

8 - The Machine Learning Process

9 - The Machine Learning Landscape

4 - Preliminary Data QA

10 - Introduction

11 - Why QA

12 - Variable Types

13 - Empty Values

14 - Range Calculations

15 - Count Calculations

16 - Left Right Censored Data

17 - Table Structure

18 - CASE STUDY Preliminary QA

19 - BEST PRACTICES Preliminary QA

5 - Univariate Profiling

20 - Introduction

21 - Categorical Variables

22 - Discretization

23 - Nominal vs Ordinal

24 - Categorical Distributions

25 - Numerical Variables

26 - Histograms Kernel Densities

27 - CASE STUDY Histograms

28 - Normal Distribution

29 - CASE STUDY Normal Distribution

30 - Univariate Data Profiling

31 - Mode

32 - Mean

33 - Median

34 - Percentile

35 - Variance

36 - Standard Deviation

37 - Skewness

38 - BEST PRACTICES Univariate Profiling

6 - Multivariate Profiling

39 - Introduction

40 - CategoricalCategorical

41 - CASE STUDY Heat Maps

42 - CategoricalNumerical

43 - Multivariate Kernel Densities

44 - Violin Plots

45 - Box Plots

46 - Limitations of Categorical Distributions

47 - NumericalNumerical

48 - Correlation

49 - Correlation vs Causation

50 - Visualizing Third Dimension

51 - CASE STUDY Correlation

52 - BEST PRACTICES Multivariate Profiling

53 - Looking Ahead to Part 2

7 - PART 2 Classification Modeling

54 - Part 2 Classification Modeling

8 - Intro to Classification

55 - Supervised vs Unsupervised Learning

56 - Classification vs Regression

57 - RECAP Key Concepts

58 - Classification 101

59 - Classification Workflow

60 - Feature Engineering

61 - Data Splitting

62 - Overfitting

9 - Classification Models

63 - Common Classification Models

64 - Intro to KNearest Neighbors KNN

65 - KNN Examples

66 - CASE STUDY KNN

67 - Intro to Naive Bayes

68 - Naive Bayes Frequency Tables

69 - Naive Bayes Conditional Probability

70 - CASE STUDY Naive Bayes

71 - Intro to Decision Trees

72 - Decision Trees Entropy 101

73 - Entropy Information Gain

74 - Decision Tree Examples

75 - Random Forests

76 - CASE STUDY Decision Trees

77 - Intro to Logistic Regression

78 - Logistic Regression Example

79 - False Positives vs False Negatives

80 - Logistic Regression Equation

81 - The Likelihood Function

82 - Multivariate Logistic Regression

83 - CASE STUDY Logistic Regression

84 - Intro to Sentiment Analysis

85 - Cleaning Text Data

86 - Bag of Words Analysis

87 - CASE STUDY Sentiment Analysis

10 - Model Selection Tuning

88 - Intro to Selection Tuning

89 - Hyperparameters

90 - Imbalanced Classes

91 - Confusion Matrix

92 - Accuracy Precision Recall

93 - Multiclass Confusion Matrix

94 - Multiclass Scoring

95 - Model Selection

96 - Model Drift

97 - Looking ahead to Part 3

11 - PART 3 Regression Forecasting

98 - Part 3 Regression Forecasting

12 - Intro to Regression

99 - Supervised vs Unsupervised Learning

100 - RECAP Key Concepts

101 - Regression 101

102 - Feature Engineering for Regression

103 - Prediction vs RootCause Analysis

13 - Regression Modeling 101

104 - Intro to Regression Modeling

105 - Linear Relationships

106 - Least Squared Error

107 - Univariate Linear Regression

108 - CASE STUDY Univariate Linear Regression

109 - Multiple Linear Regression

110 - NonLinear Regression

111 - CASE STUDY NonLinear Regression

14 - Model Diagnostics

112 - Intro to Model Diagnostics

113 - Sample Model Output

114 - RSquared

115 - Mean Error Metrics MSE MAE MAPE

116 - Homoskedasticity

117 - Null Hypothesis

118 - FSignificance

119 - TValues PValues

120 - Multicollinearity

121 - Variance Inflation Factor

122 - RECAP Sample Model Output

15 - TimeSeries Forecasting

123 - Intro to Forecasting

124 - Seasonality

125 - Auto Correlation Function

126 - CASE STUDY Seasonality with ACF

127 - OneHot Encoding

128 - CASE STUDY Seasonality with OneHot Encoding

129 - Linear Trending

130 - CASE STUDY Seasonality with Linear Trend

131 - Smoothing

132 - CASE STUDY Smoothing

133 - NonLinear Trends

134 - CASE STUDY NonLinear Trend

135 - Intervention Analysis

136 - CASE STUDY Intervention Analysis

137 - Looking Ahead to Part 4

16 - PART 4 Unsupervised Learning

138 - Part 4 Unsupervised Learning

17 - Intro to Unsupervised ML

139 - Supervised vs Unsupervised Learning

140 - Common Unsupervised Techniques

141 - Unsupervised ML Workflow

142 - RECAP Feature Engineering

143 - KEY TAKEAWAYS Intro to Unsupervised ML

18 - Clustering Segmentation

144 - Introduction

145 - Clustering Basics

146 - Intro to KMeans

147 - WSS Elbow Plots

148 - KMeans FAQs

149 - CASE STUDY KMeans

150 - Intro to Hierarchical Clustering

151 - Anatomy of a Dendrogram

152 - Hierarchical Clustering FAQs

153 - KEY TAKEAWAYS Clustering Segmentation

19 - Association Mining Basket Analysis

154 - Introduction

155 - Association Mining Basics

156 - The Apriori Algorithm

157 - Basket Analysis Examples

158 - Minimum Support Thresholds

159 - Infrequent Itemsets

160 - Multiple Item Sets

161 - CASE STUDY Apriori

162 - Markov Chains

163 - CASE STUDY Markov Chains

164 - KEY TAKEAWAYS Association Mining

20 - Outlier Detection

165 - Introduction

166 - Outlier Detection Basics

167 - CrossSectional Outliers

168 - CrossSectional Outlier Example

169 - CASE STUDY CrossSectional Outlier

170 - TimeSeries Outliers

171 - TimeSeries Outlier Example

172 - KEY TAKEAWAYS Outlier Detection

21 - Dimensionality Reduction

173 - Introduction

174 - Dimensionality Reduction Basics

175 - Principle Component Analysis

176 - PCA Example

177 - Interpreting Components

178 - Scree Plots

179 - Advanced Techniques

180 - KEY TAKEAWAYS Dimensionality Reduction

22 - Wrapping Up

181 - Series Conclusion

182 - BONUS LESSON.html

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