CF

Introduction to Regression Analysis

6h 20m 25s
English
Paid

This course gives you a clear and hands-on start with regression analysis. You learn how each model works and how to use it in real projects.

What You Learn

You work with key regression models in data science. These include linear, logistic, logarithmic, and the Cox model. You see how they work in Python and why you would pick one over another.

How You Learn

You use real datasets and follow short, clear steps. Each topic comes with practical tasks, so you can test ideas right away. You also explore feature selection, model bias, overfitting, and how to read model results. You get a simple entry into survival analysis as well.

Projects

You finish the course with capstone projects. These bring all ideas together and help you show your skills. They prepare you for work in data analysis, data science, and machine learning.

About the Author: Zero To Mastery

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Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

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#1: Introduction to Regression Analysis
All Course Lessons (82)
#Lesson TitleDurationAccess
1
Introduction to Regression Analysis Demo
02:40
2
Game Plan for Multilinear Regression
01:23
3
CASE STUDY Briefing - Pricing Diamonds
01:54
4
Linear Regression
05:13
5
Python - Libraries and Data
02:28
6
Python - Exploratory Data Analysis
03:19
7
Python - Linear Regression
02:17
8
Regression Statistics
04:24
9
Python - Plotting Regression Curve
07:08
10
Dummy Variable (Trap)
04:01
11
Python - Linear Regression with Dummy Variables
07:16
12
EXERCISE: Create Function that Reads the Regression Coefficients
09:36
13
CASE STUDY - Linearity Bias - We Will All Be Obese! Wait What?
04:02
14
Multilinear Regression
01:48
15
Python - Categorical Variables
05:40
16
Under and Overfitting
03:28
17
Training and Test Set
02:35
18
Python - Multilinear Regression
03:45
19
Assessing Regression Models
06:08
20
Python - Assessing Regression Model
03:48
21
CASE STUDY - Dangers of Regression Analysis
02:52
22
Multilinear Regression Wrap Up
02:03
23
Captone Project - Understanding Sales Drivers
01:20
24
Python - Solutions - Step 1
07:21
25
Python - Solutions - Step 2-4
04:30
26
Python - Solutions - Step 5-6
03:48
27
Game Plan for Logistic Regression
01:39
28
CASE STUDY Briefing - Spam Emails
01:26
29
Logistic Regression
03:29
30
Python - Preparing Script and Loading Data
03:32
31
Python - Summary Statistics
03:45
32
Python - Histograms and Outlier Detection
05:37
33
Python - Correlation Matrix
03:27
34
Python - Logistic Regression Preparation
04:00
35
How to Read Logistic Regression Coefficients
02:12
36
Python - Logistic Regression
02:18
37
Python - Build a Coefficient Function with ChatGPT
09:07
38
Python - Predictions
03:20
39
Confusion Matrix and Model Assessment
06:25
40
Python - Confusion Matrix and Classification Report
05:35
41
Python - Assessing Classification Models with ChatGPT
05:31
42
Section Wrap Up - Logistic Regression
03:16
43
Capstone Project - Surviving Titanic
01:03
44
Python - Libraries and Data
08:21
45
Python - Removing Outliers and EDA
06:33
46
Python - Logistic Regression Model and Assessment
06:07
47
Game Plan for Cox Proportional Hazard Regression
02:15
48
Introduction to Survival Analysis
07:48
49
CASE STUDY - Briefing
01:48
50
Python - Libraries and Data
05:10
51
Kaplan-Meier Estimator
04:36
52
Python - Kaplan Meier Estimator
04:23
53
Python - Calculating for a Specific Event
02:47
54
Python - Plotting Kaplan-Meier and Cumulated Curves
03:52
55
Censoring
03:46
56
Log Rank Test
02:56
57
Python - Kaplan-Meier Estimator per Gender and Visualization
05:51
58
Python - Log Rank Test
06:34
59
Cox Proportional Hazard Regression
04:52
60
Python - Prepare Data for CPH Model
03:12
61
Python - Cox Proportional Hazard Regression
09:37
62
Python - Visualize Results
02:13
63
Assessing Cox Proportional Hazard Models
05:19
64
Python - Assessing the CPH Model
08:38
65
Python - Predicting Specific Instances
03:39
66
Cox Proportional Hazard Regression Wrap Up
03:15
67
Capstone Project - Will Your App Make it?
01:24
68
Python - Libraries and Data
07:11
69
Python - Data Cleaning
19:11
70
Python - Dependent Variable
08:27
71
Python - Kaplan-Meier Estimator
04:31
72
Python - Cox Model
10:01
73
Game Plan for Logarithmic Regression
04:20
74
Python - Logarithmic Regression Setup
04:06
75
Python - Data Prep and Visualization
06:12
76
Python - Normal Linear Regression
04:43
77
Python - Plotting Normal Linear Regression
04:00
78
Python - Linear - Log Regression
05:43
79
Python - Log - Linear Regression
05:43
80
Python - Log - Binary
06:37
81
Python - Log-Log Regression
03:23
82
Let's Keep Learning Together!
00:52
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Frequently asked questions

What is Introduction to Regression Analysis about?
This course gives you a clear and hands-on start with regression analysis. You learn how each model works and how to use it in real projects. What You Learn You work with key regression models in data science. These include linear…
Who teaches this course?
It is taught by Zero To Mastery. You can find more courses by this instructor on the corresponding source page.
How long is the course?
It contains 82 lessons with a total runtime of 6 hours 20 minutes. Every lesson is available to watch online at your own pace.
Is it free to watch?
It is part of CourseFlix's premium catalog. A subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch it online?
The course is available to watch online on CourseFlix at https://courseflix.net/course/introduction-to-regression-analysis. The page hosts every lesson with the integrated video player; no download is required.