CF

Introduction to Regression Analysis

6h 20m 25s
English
Paid

Introduction to Regression Analysis is a 82-lesson 6 hours 20 minutes self-paced course by Zero To Mastery. This course gives you a clear and hands-on start with regression analysis.

Course facts

Lessons
82
Duration
6 hours 20 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

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.

Who teaches Introduction to Regression Analysis? Zero To Mastery

Zero To Mastery thumbnail

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.

What lessons are included in Introduction to Regression Analysis?

This is a demo lesson (10:00 remaining)

You can watch up to 10 minutes for free. Subscribe to unlock all 82 lessons in this course and access 10,000+ hours of premium content across all courses.

View Pricing
0:00
/
#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
Unlock unlimited learning

Get instant access to all 81 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.

Learn more about subscription

What courses are similar to Introduction to Regression Analysis?

Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling in this course, it's beneficial to have a basic understanding of Python programming, as the course involves using Python for data analysis and modeling. Familiarity with fundamental statistics concepts will also be advantageous, given that the course covers topics like regression statistics, model bias, and overfitting.
What kind of projects will I work on during the course?
The course includes capstone projects that integrate all the concepts learned throughout the lessons. You will work on projects such as understanding sales drivers and analyzing survival data from the Titanic dataset. These projects are designed to showcase your skills in data analysis, data science, and machine learning.
Is this course suitable for beginners in data science?
This course is designed to provide a hands-on start with regression analysis, making it suitable for beginners in data science who have some prior knowledge of Python and basic statistics. It offers a practical approach to learning through real datasets and exercises, making it accessible to those new to the field.
How does this course compare in depth and scope to other regression analysis courses?
The course offers a practical and hands-on approach, focusing on key regression models like linear, logistic, and Cox models. It emphasizes the application of these models in Python, with lessons on exploratory data analysis, model assessment, and survival analysis. The scope includes foundational topics such as feature selection and overfitting, which are crucial for understanding and applying regression analysis in data science.
What tools and platforms are used in this course?
The course primarily uses Python as the programming language for data analysis and modeling. It includes lessons on Python libraries and data handling, exploratory data analysis, and plotting regression curves. Additionally, the course utilizes tools like ChatGPT for building coefficient functions and assessing classification models.
What topics are not covered in this course?
While the course covers a broad range of regression analysis topics, it does not delve into non-regression machine learning algorithms such as decision trees, neural networks, or clustering methods. The focus remains on regression models and their applications in data science.
How can the skills learned in this course be applied to other areas or courses?
The skills gained from this course, such as understanding regression models, feature selection, and handling data in Python, are highly transferable to various fields within data science and machine learning. They provide a solid foundation for more advanced courses or careers that require data analysis, predictive modeling, or statistical analysis.