English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 152 lectures (8h 30m) | 3.18 GB
Learn Python for Data Science & Machine Learning, and build regression and forecasting models with hands-on projects
This is a hands-on, project-based course designed to help you master the foundations for regression analysis in Python.
We’ll start by reviewing the data science workflow, discussing the primary goals & types of regression analysis, and do a deep dive into the regression modeling steps we’ll be using throughout the course.
You’ll learn to perform exploratory data analysis, fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We’ll also review the assumptions of linear regression, and learn how to diagnose and fix each one.
From there, we’ll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, tuning, and model selection. You’ll also learn how to improve model performance by leveraging feature engineering techniques and regularized regression algorithms.
Throughout the course, you’ll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you’ll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them.
Last but not least, you’ll get an introduction to time series analysis & forecasting techniques. You’ll learn to analyze trends & seasonality, perform decomposition, and forecast future values.
COURSE OUTLINE:
Intro to Data Science
Introduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflow
Regression 101
Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflow
Pre-Modeling Data Prep & EDA
Recap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationships
Simple Linear Regression
Build simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and output
Multiple Linear Regression
Build multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metrics
Model Assumptions
Review the assumptions of linear regression models that need to be met to ensure that the model’s predictions and interpretation are valid
Model Testing & Validation
Test model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test data
Feature Engineering
Apply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and more
Regularized Regression
Introduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regression
Time Series Analysis
Learn methods for exploring time series data and how to perform time series forecasting using linear regression and Facebook Prophet
What you’ll learn
- Master the machine learning foundations for regression analysis in Python
- Perform exploratory data analysis on model features, the target, and relationships between them
- Build and interpret simple and multiple linear regression models with Statsmodels and Scikit-Learn
- Evaluate model performance using tools like hypothesis tests, residual plots, and mean error metrics
- Diagnose and fix violations to the assumptions of linear regression models
- Tune and test your models with data splitting, validation and cross validation, and model scoring
- Leverage regularized regression algorithms to improve test model performance & accuracy
- Employ time series analysis techniques to identify trends & seasonality, perform decomposition, and forecast future values
Table of Contents
Getting Started
1 Course Introduction
2 About This Series
3 Course Structure Outline
4 READ ME Important Notes for New Students
5 DOWNLOAD Course Resources
6 Introducing the Course Project
7 Setting Expectations
8 Jupyter Installation Launch
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
Regression 101
22 Regression 101
23 Goals of Regression
24 Types of Regression
25 Regression Modeling Workflow
26 Key Takeaways
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
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
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
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
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
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
Project 1 San Francisco Rent Prices
108 Project Brief
109 Solution Walkthrough
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
Project 1 San Francisco Rent Prices Continued
130 Project Brief
131 Solution Walkthrough
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
Project 2 Electricity Consumption
150 Project Brief
151 Solution Walkthrough
Next Steps
152 EXTRA LESSON
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