Introduction
Machine Learning 101
Machine Learning and Data Science Framework

11Section Overview

12Introducing Our Framework

136 Step Machine Learning Framework

14Types of Machine Learning Problems

15Types of Data

16Types of Evaluation

17Features In Data

18Modelling Splitting Data

19Modelling Picking the Model

20Modelling Tuning

21Modelling Comparison

22Experimentation

23Tools We Will Use
The 2 Paths
Data Science Environment Setup
Pandas Data Analysis

35Section Overview

36Pandas Introduction

37Series Data Frames and CSVs

38Describing Data with Pandas

39Selecting and Viewing Data with Pandas

40Selecting and Viewing Data with Pandas Part 2

41Manipulating Data

42Manipulating Data 2

43Manipulating Data 3

44How To Download The Course Assignments
NumPy

45NumPy Introduction

46NumPy DataTypes and Attributes

47Creating NumPy Arrays

48NumPy Random Seed

49Viewing Arrays and Matrices

50Manipulating Arrays

51Manipulating Arrays 2

52Standard Deviation and Variance

53Reshape and Transpose

54Dot Product vs Element Wise

55Exercise Nut Butter Store Sales

56Comparison Operators

57Sorting Arrays

58Turn Images Into NumPy Arrays
Matplotlib Plotting and Data Visualization

59Section Overview

60Matplotlib Introduction

61Importing And Using Matplotlib

62Anatomy Of A Matplotlib Figure

63Scatter Plot And Bar Plot

64Histograms And Subplots

65Subplots Option 2

66Quick Tip Data Visualizations

67Plotting From Pandas DataFrames

68Plotting From Pandas DataFrames 2

69Plotting from Pandas DataFrames 3

70Plotting from Pandas DataFrames 4

71Plotting from Pandas DataFrames 5

72Plotting from Pandas DataFrames 6

73Plotting from Pandas DataFrames 7

74Customizing Your Plots

75Customizing Your Plots 2

76Saving And Sharing Your Plots
Scikit learn Creating Machine Learning Models

77Section Overview

78Scikit learn Introduction

79Refresher What Is Machine Learning

80Scikit learn Cheatsheet

81Typical scikit learn Workflow

82Optional Debugging Warnings In Jupyter

83Getting Your Data Ready Splitting Your Data

84Quick Tip Clean Transform Reduce

85Getting Your Data Ready Convert Data To Numbers

86Getting Your Data Ready Handling Missing Values With Pandas

87Getting Your Data Ready Handling Missing Values With Scikit learn

88Choosing The Right Model For Your Data

89Choosing The Right Model For Your Data 2 (Regression)

90Quick Tip How ML Algorithms Work

91Choosing The Right Model For Your Data 3 (Classification)

92Fitting A Model To The Data

93Making Predictions With Our Model

94predict vs predict proba

95Making Predictions With Our Model (Regression)

96Evaluating A Machine Learning Model (Score)

97Evaluating A Machine Learning Model 2 (Cross Validation)

98Evaluating A Classification Model 1 (Accuracy)

99Evaluating A Classification Model 2 (ROC Curve)

100Evaluating A Classification Model 4 (Confusion Matrix)

101Evaluating A Classification Model 5 (Confusion Matrix)

102Evaluating A Classification Model 6 (Classification Report)

103Evaluating A Regression Model 1 (R2 Score)

104Evaluating A Regression Model 2 (MAE)

105Evaluating A Regression Model 3 (MSE)

106Evaluating A Model With Cross Validation and Scoring Parameter

107Evaluating A Model With Scikit learn Functions

108Improving A Machine Learning Model

109Tuning Hyperparameters

110Tuning Hyperparameters 2

111Tuning Hyperparameters 3

112Quick Tip Correlation Analysis

113Saving And Loading A Model 2

114Putting It All Together

115Putting It All Together 2
Milestone Project 1 Supervised Learning (Classification)

116Section Overview

117Project Overview

118Project Environment Setup

119Optional Windows Project Environment Setup

120Step 1 4 Framework Setup

121Getting Our Tools Ready

122Exploring Our Data

123Finding Patterns

124Finding Patterns 2

125Preparing Our Data For Machine Learning

126Choosing The Right Models

127Experimenting With Machine Learning Models

128TuningImproving Our Model

129Tuning Hyperparameters

130Tuning Hyperparameters 2

131Tuning Hyperparameters 3

132Evaluating Our Model

133Evaluating Our Model 2

134Evaluating Our Model 3

135Finding The Most Important Features

136Reviewing The Project
Milestone Project 2 Supervised Learning (Time Series Data)

137Section Overview

138Project Overview

139Project Environment Setup

140Step 1 4 Framework Setup

141Exploring Our Data

142Exploring Our Data 2

143Feature Engineering

144Turning Data Into Numbers

145Filling Missing Numerical Values

146Filling Missing Categorical Values

147Fitting A Machine Learning Model

148Splitting Data

149Custom Evaluation Function

150Reducing Data

151Randomized Search CV

152Improving Hyperparameters

153Preproccessing Our Data

154Making Predictions

155Feature Importance
Data Engineering

156Data Engineering Introduction

157What Is Data

158What Is A Data Engineer

159What Is A Data Engineer 2

160What Is A Data Engineer 3

161What Is A Data Engineer 4

162Types Of Databases

163Optional OLTP Databases

164Hadoop HDFS and MapReduce

165Apache Spark and Apache Flink

166Kafka and Stream Processing
Neural Networks Deep Learning Transfer Learning and Tensor Flow 2

167Section Overview

168Deep Learning and Unstructured Data

169Setting Up Google Colab

170Google Colab Workspace

171Uploading Project Data

172Setting Up Our Data

173Setting Up Our Data 2

174Importing TensorFlow 2

175Optional TensorFlow 2.0 Default Issue

176Using A GPU

177Optional GPU and Google Colab

178Optional Reloading Colab Notebook

179Loading Our Data Labels

180Preparing The Images

181Turning Data Labels Into Numbers

182Creating Our Own Validation Set

183Preprocess Images

184Preprocess Images 2

185Turning Data Into Batches

186Turning Data Into Batches 2

187Visualizing Our Data

188Preparing Our Inputs and Outputs

189Building A Deep Learning Model

190Building A Deep Learning Model 2

191Building A Deep Learning Model 3

192Building A Deep Learning Model 4

193Summarizing Our Model

194Evaluating Our Model

195Preventing Overfitting

196Training Your Deep Neural Network

197Evaluating Performance With TensorBoard

198Make And Transform Predictions

199Transform Predictions To Text

200Visualizing Model Predictions

201Visualizing And Evaluate Model Predictions 2

202Visualizing And Evaluate Model Predictions 3

203Saving And Loading A Trained Model

204Training Model On Full Dataset

205Making Predictions On Test Images

206Submitting Model to Kaggle

207Making Predictions On Our Images