CF

Introduction to Regression Analysis

6h 20m 25s
English
Free

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
Free

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?

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

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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.