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How to find a job with Regression skills

How is Regression used?

Zippia reviewed thousands of resumes to understand how regression is used in different jobs. Explore the list of common job responsibilities related to regression below:

  • Performed Regression Testing on different modules to check compatibility and performance of application using Win Runner.
  • Performed Configuration testing, Functional testing and Regression Testing.
  • Performed Regression Testing when necessary.
  • Performed Regression Testing, System Testing, Functional Testing, UAT, Gap Analysis, GxP Risk Assessment of the system.
  • Created discrepancy reports (Issue Log, Incident Report) and performed regression testing to validate incorporated fixes to software.
  • Performed Data validation using SQL Queries from front end to the back end and performed End-to-End Testing and Regression Testing.

Are Regression skills in demand?

Yes, regression skills are in demand today. Currently, 6,933 job openings list regression skills as a requirement. The job descriptions that most frequently include regression skills are validation analyst, principal product manager, and data product manager.

How hard is it to learn Regression?

Based on the average complexity level of the jobs that use regression the most: validation analyst, principal product manager, and data product manager. The complexity level of these jobs is challenging.

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What jobs can you get with Regression skills?

You can get a job as a validation analyst, principal product manager, and data product manager with regression skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with regression skills.

Validation Analyst

Job description:

Validation Analysts are responsible for the accurate review and assessments of systems and processes within an organization. For example, a model valuation analyst has the task of assessing quantitative rules and statistical models that will help banks manage credit risks and other financial risks. Their duties include evaluation of risk models, developing validation plans, conduct statistical testing, and inspecting data. They also liaise with internal stakeholders to resolve model issues, maintain findings documentation as well as prepare report for senior management.

  • QC
  • Regression
  • SQL
  • Validation Documentation
  • Corrective Action
  • Validation Process

Principal Product Manager

Job description:

The duties of a principal product manager depend on one's place or industry of employment. Typically, their responsibilities revolve around overseeing the organization's product roadmap, devising strategies for optimal operations and services, and managing production and marketing communications. They must also coordinate with different research scientists and engineers to develop systems and networks that will increase profitability and assist in meeting consumers' needs. Furthermore, as a manager, it is essential to lead and encourage teams, all while implementing the company's policies and regulations.

  • Product Management
  • QA
  • Product Development
  • Regression
  • User Stories
  • Saas

Data Product Manager

Job description:

A data product manager is responsible for supervising the data flow within the product management process, ensuring that the data address the product's features and functionality. Data product managers analyze data statistics by conducting surveys and reviewing reports, adjusting business plans according to the results. They also coordinate with the clients to inform them of progress updates and assist with their additional requirements and requests. A data product manager must have excellent communication and organizational skills, especially in performing quality control procedures before releasing final product outputs.

  • Product Management
  • BI
  • QA
  • Regression
  • Product Roadmap
  • Project Management

Consultant-Software Tester

  • Java
  • Test Results
  • Regression
  • User Acceptance
  • Test Scenarios
  • Cucumber

Junior Quality Assurance Analyst/Analyst

Job description:

Junior quality assurance (QA) analysts execute automated and manual testing, exposing software application bugs, and evaluating user experience and interface design. The QA analysts document data and report their findings to software programmers and developers assigned to the product. They distinguish importance and have a better understanding of the patterns and trends from different sources like government sources, online news, and blogs. The knowledge and skills they need to acquire include test scripts, quality assurance, and software development.

  • QA
  • Regression
  • Manual Test Cases
  • Development Life
  • Jira
  • Test Scripts

Software Quality Analyst

Job description:

A software quality analyst troubleshoots bugs for a software application. They are responsible for certifying the quality of applications and software. They also evaluate functional problems through regression testing, and they create test strategies based on project requirements. They interact with developers, project managers, customers, and stakeholders.

  • Regression
  • QA
  • SQL Server
  • Manual Test Cases
  • Test Scripts
  • Scrum

Software Tester

Job description:

The responsibility of software testers involves the quality assurance of software development and deployment. Software testers conduct manual and automated tests to make sure that the software is developed for its purpose. They remove the issues and bugs within a product before deployment to users. Their responsibilities include software and systems analysis, risk mitigation, and software-related issue prevention. They should have skills in automation, programming, social networking, logical thinking, and mobile and web technology.

  • Test Results
  • Regression
  • Test Scripts
  • Jira
  • Manual Test Cases
  • QA

Quality Analyst And Tester

  • QA
  • Regression
  • Manual Test Cases
  • QC
  • Test Results
  • Qa Testing

Junior Quality Assurance Tester

Job description:

A junior quality assurance tester is responsible for facilitating different quality assurance, such as integration and performance testing of software systems. They handle the communication process between stakeholders and clients and resolve issues like bugs, viruses, and other unwanted software. They also develop and maintain a collaborative partnership with different agencies and software developers. They usually work with a supervising quality assurance tester and with other business groups to complete testing.

  • QA
  • Manual Test Cases
  • Regression
  • SQL Server
  • Test Plan
  • Web Application

Product Manager Lead

Job description:

A product manager lead is responsible for supervising the development and strategic launch of the company's products from conceptualization to final market release. Product managers work closely with the marketing and sales team to identify efficient promotional techniques to enhance the brand's image across the market and develop outstanding features according to trends and public demands. They also handle media commitments and press releases and maximize product vision. A product manager lead directs the budgeting and sales forecasting, ensuring that the process adheres to budget limitations and deadlines.

  • Architecture
  • QA
  • Product Management
  • Product Strategy
  • Regression
  • Lifecycle Management

UAT Tester

Job description:

User acceptance testing (UAT) plays a vital part in ensuring that software systems, applications, and other devices function efficiently, which is why a UAT tester must carry out a variety of tasks to make this possible. Among their responsibilities include studying the product or projects' specifications, developing test plans and structures to ensure that products are user-friendly, gathering and analyzing data, establishing test guidelines and timelines, and coordinating with other testers. Moreover, a UAT tester implements test structures and maintains records of all procedures, all while adhering to the company's policies and regulations.

  • Test Scripts
  • Regression
  • End Testing
  • Manual Test Cases
  • Test Scenarios
  • QA

Quality Assurance Tester

Job description:

A quality assurance tester, also known as QA tester, has different responsibilities depending on the line of work or industry involved. In a computer development setting, they are in charge of working and coordinating with technical engineers. They have to conduct regular tests for new software and programs, identify inconsistencies, and find areas for improvement. Furthermore, a quality assurance tester must produce reports from the tests, which can be the basis of progress for further developments.

  • Manual Test Cases
  • Regression
  • Test Scripts
  • Selenium Webdriver
  • Java
  • Status Reports

Junior Quality Assurance Engineer

Job description:

In a manufacturing setting, a junior quality assurance engineer specializes in developing and executing quality assurance test procedures in adherence to the company's standards and regulations. They are typically in charge of reviewing production plans and designs, identifying the strengths and weaknesses of current methods, addressing issues and concerns, and performing corrective measures in a timely manner. Furthermore, as a junior quality assurance engineer, it is essential to maintain an active communication line with staff, coordinating every quality assurance activity.

  • Java
  • Jira
  • Regression
  • JavaScript
  • Test Scripts
  • UI

Ground Systems Engineer

  • Systems Engineering
  • Software Development
  • DOD
  • NASA
  • Regression
  • Linux

Quality Assurance Analyst

Job description:

Quality Administrators are responsible for managing the quality processes of an organization. Their duties include creating data collection processes, conducting data cleansing, tracks quality milestones, and develop training programs for internal teams. They undertake daily audits, work with the quality coordinator to determine root cause, and assist with feedback and complaints. Quality administrators also write daily reports for purchase orders that are overdue, open service jobs, quotations needing to follow up, unconfirmed hires, and processed sales orders.

  • QA
  • Regression
  • Manual Test Cases
  • Test Scripts
  • Test Results
  • Java

Oracle Application Developer

  • Java
  • JavaScript
  • Apex
  • Oracle Sql
  • Regression
  • Debugging

Senior Software Quality Analyst

  • QA
  • Regression
  • Manual Test Cases
  • Test Scripts
  • Java
  • SQL Server

Senior Software Tester

  • Java
  • Test Scripts
  • Regression
  • Test Results
  • QA
  • Apache Tomcat

Database Tester

  • ETL
  • SQL Server
  • Regression Test Cases
  • Test Plan
  • Regression
  • Back-End

Quality Tester

Job description:

Quality technician engineers work to develop high-quality practices that will help produce quality products and services. The quality technician engineer will work with managers in developing solid quality-checking practices ensuring consistency and quality. The quality technician engineer is also responsible for working with other team members in the quality assurance team to establish consumer trust. The QT engineer also acts as part of the quality control team and provides suggestions to improve the product or service.

  • Test Results
  • Test Scripts
  • QA
  • Regression
  • Data Integrity
  • Java

How much can you earn with Regression skills?

You can earn up to $84,626 a year with regression skills if you become a validation analyst, the highest-paying job that requires regression skills. Principal product managers can earn the second-highest salary among jobs that use Python, $145,943 a year.

Job TitleAverage SalaryHourly Rate
Validation Analyst$84,626$41
Principal Product Manager$145,943$70
Data Product Manager$117,599$57
Consultant-Software Tester$99,356$48
Junior Quality Assurance Analyst/Analyst$67,999$33

Companies using Regression in 2025

The top companies that look for employees with regression skills are Meta, Old Dominion Freight Line, and Oracle. In the millions of job postings we reviewed, these companies mention regression skills most frequently.

RankCompany% Of All SkillsJob Openings
1Meta18%9,540
2Old Dominion Freight Line10%2,069
3Oracle9%35,495
4CTG8%227
5U.S. Bank7%3,062

Departments using Regression

DepartmentAverage Salary
Engineering$98,656
Plant/Manufacturing$78,069

20 courses for Regression skills

Advertising Disclosure

1. Regression and Classification

coursera

Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder...

2. Machine Learning: Regression

coursera

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python...

3. Regression Analysis / Data Analytics in Regression

udemy
4.5
(1,165)

November, 2019. Get marketable and highly sought after skills in this course while substantially increasing your knowledge of data analytics in regression. All course videos created and narrated by an award winning instructor and textbook author of quantitative methods. This course covers running and evaluating linear regression models (simple regression, multiple regression, and hierarchical regression), including assessing the overall quality of models and interpreting individual predictors for significance. R-Square is explored in depth, including how to interpret R-Square for significance. Together with coverage of simple, multiple and hierarchical regression, we'll also explore correlation, an important statistical procedure that is closely related to regression. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Examples of running regression in both SPSS and Excel programs provided. Lectures provided in high quality, HD video with course quizzes available to help cement the concepts. Taught by a PhD award-winning university instructor with over 15 years of teaching experience. At Quantitative Specialists, our highest priority is in creating crystal-clear, accurate, easy-to-follow videos. Tame the regression beast once and for all - enroll today!...

4. Linear Regression and Logistic Regression in Python

udemy
4.4
(429)

You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right?You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning. Create a linear regression and logistic regression model in Python and analyze its result. Confidently model and solve regression and classification problemsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, which is Linear Regression and Logistic RegregressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear and logistic regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i. e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1! CheersStart-Tech Academy------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i. e., when the target variable is a real value. Linear regression is a linear model, e. g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear and Logistic Regression modelling - Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn't always been, Python is the programming language of choice for data science. Here's a brief history:    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.    In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools.    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it's nice to know that employment opportunities are abundant (and growing) as well...

5. Supervised Machine Learning: Regression

coursera

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics...

6. Linear Regression and Modeling

coursera

This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio...

7. Linear Regression with Python

coursera

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process. Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

8. Linear Regression and Logistic Regression using R Studio

udemy
4.4
(288)

You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in R Studio, right?You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning. Create a linear regression and logistic regression model in R Studio and analyze its result. Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i. e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! CheersStart-Tech Academy------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i. e., when the target variable is a real value. Linear regression is a linear model, e. g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn't always been, Python is the programming language of choice for data science. Here's a brief history:    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.    In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools.    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it's nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge-and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning...

9. Understanding Regression Techniques

udemy
3.9
(119)

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies...

10. Linear Regression for Business Statistics

coursera

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel. The focus of the course is on understanding and application, rather than detailed mathematical derivations. Note: This course uses the ‘Data Analysis’ tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac. WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model. Topics covered include: • Introducing the Linear Regression • Building a Regression Model and estimating it using Excel • Making inferences using the estimated model • Using the Regression model to make predictions • Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the ‘Dummy variable regression’ which is used to incorporate categorical variables in a regression. Topics covered include: • Hypothesis testing in a Linear Regression • ‘Goodness of Fit’ measures (R-square, adjusted R-square) • Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it. Topics covered include: • Dummy variable Regression (using Categorical variables in a Regression) • Interpretation of coefficients and p-values in the presence of Dummy variables • Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as ‘Interaction variables’ is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. Topics covered include: • Mean centering of variables in a Regression model • Building confidence bounds for predictions using a Regression model • Interaction effects in a Regression • Transformation of variables • The log-log and semi-log regression models...

11. Multiple Regression with Minitab

udemy
4.6
(98)

In this course, I will teach you one of the most commonly used analytical techniques: Regression Analysis. This course covers the top of multiple regression analysis at the Six Sigma Master Black Belt level. I will use Minitab 19 to perform the analysis. The focus of my teaching will be on explaining the concepts and on analyzing and interpreting the results of the analysis. The course starts from the basics, covering the scatter plot and learning the simple regression with just one predictor. The analysis is conducted in Minitab 19, and the results of the output are explained in detail. To understand the concept, a simple example of hours of studies and marks obtained in the exam is taken. As you move through the course the example becomes more complex. In the end, we analyzed and modelled the insurance cost based on various factors. This course also covers hypothesis testing, understanding the p-value to interpret the result. Later, additional predictors are added to the regression model. The performance of the model is understood by interpreting the value of R-squared and adjusted R-squared. The following concepts are covered in this course: Simple Linear RegressionMultiple RegressionNonlinear Regression (Polynomial)Bias Variance Trade-offSelecting features using Best Subsets and Stepwise selection approachesIdentifying OutliersTraining and Test Data - Validation set approach, Leave one out cross-validation and K-Fold Validation. Predicting ResponseProject Work - Medical Insurance Charges...

12. Linear Regression: Absolute Fundamentals

udemy
4.1
(103)

Hello, everybody! The Machine Learning Absolute Fundamentals for Linear Regression course is open to all students. Beginner Python developers who wish to begin their machine learning adventure should take this course. In this lesson, we'll apply a linear regression model from the Python scikit-learn module to forecast the total number of COVID19 positive cases in a specific Indian state. You'll be able to: once you've finished this course.1. Explain what machine learning is2. Describe what a dataset is.3. Describe the functions of machine learning.4. Explanation of the linear regression concept5. Describe the cost function and the line of greatest fit (MSE)6. To read and prepare the dataset, use the pandas library functions.7. Data division for training and testing8. Using Sklearn, develop a linear regression model and train it.9. Assess the model and make value predictions10. Data visualisation with MatplotlibIn linear regression, linear predictor functions are used to model relationships, with the model's unknown parameters being estimated from the data. These models are referred to as linear models. The conditional mean of the response is typically considered to be an affine function of the values of the explanatory variables (or predictors)  the conditional median or another quantile is occasionally employed. In common with all other types of regression analysis, linear regression concentrates on the conditional probability distribution of the response given the values of the predictors rather than the joint probability distribution of all these variables, which is the purview of multivariate analysis...

13. Logistic Regression using Stata

udemy
4.8
(142)

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations. In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions...

14. Logistic Regression in Python

udemy
4.6
(854)

You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in Python, right?You've found the right Classification modeling course! After completing this course you will be able to: Identify the business problem which can be solved using Classification modeling techniques of Machine Learning. Create different Classification modelling model in Python and compare their performance. Confidently practice, discuss and understand Machine Learning conceptsHow this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNNWhy should you choose this course?This course covers all the steps that one should take while solving a business problem using classification techniques. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i. e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a classification model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Classification Machine Learning models: Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data Pre-processingIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation. Section 5 - Classification ModelsThis section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors. We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a classification model in Python will soar. You'll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! CheersStart-Tech Academy------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Which all classification techniques are taught in this course?In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques: Logistic RegressionLinear Discriminant AnalysisK - Nearest Neighbors (KNN) How much time does it take to learn Classification techniques of machine learning?Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification. What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of  models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn't always been, Python is the programming language of choice for data science. Here's a brief history:    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.    In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools.    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it's nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge-and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning...

15. Linear Regression using Stata

udemy
4.2
(165)

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course. In the second part of the course, students will learn how to apply what they learned using Stata. In this part, students will use Stata to fit multiple regression models, produce graphs that describe model fit and assumptions, and to use variable specific commands that will make the output more readable. This part assumed very basic knowledge of Stata...

16. Past Life Regression Course

udemy
4.8
(50)

!! NOW COMES WITH A MAGICK & WITCHCRAFT ACADEMY E-CERTIFICATE OF COMPLETION!! Are you interested in learning how to conduct a past life regression session on yourself and others? Our Past Life Regression Course is designed to help you unlock the powerful benefits of unlocking the past to understand the present. With our course, you'll learn how to conduct a safe and effective past life regression session, and gain insight into how your past experiences are influencing your current life. You will gain the knowledge and skills to help yourself and others unlock the mysteries of the past, and unlock pathways to personal and spiritual growth. Our course will guide you through each step of the process, from the basics of past life regression to advanced techniques, so that you can confidently and safely help yourself and others explore the hidden depths of their lives. Past Life Regression is a hypnotherapy-style session that leads the individual into their past life memories in order to help them find the missing puzzle piece that they need to be able to emotionally heal, find closure, or gain insight from long lost wisdom. It can be incredible help in removing internal obstacles. This course is primarily made for two types of people, one who would like to become a Past Life Regression Practitioner, so they can help others, friends, or even paid clients on their journey into their past life and second, who would like to learn how to do the regression on themselves, without the help of a therapist. This course includes a beautiful, downloadable Session Evaluation Sheet for your Client Session and a printable Cheat Sheet with scripted, fail-proof questions so that you never go blank during a session again. In this course, you will learn, step-by-step, how to conduct a Past Life Regression Session, and what you will need to do in order to become an excellent Past Life Regression Practitioner. If you would like to experience a Past Life Regression Session yourself, this course also includes a professionally recorded, downloadable Past Life Regression Guided Meditation that will lead you through a session with ease, even if you don't have any previous experience, so you can experience the same benefits from it like from a professional session. In this course, you will find: Close to 3 hours of practical video lessonsA beautiful Downloadable Session Evaluation SheetA scripted, fail-proof Cheat Sheet to help guide you through sessionsLearn how to deal with difficult emotional situationsLearn how to induce a light trance stateDownloadable Guided Meditation for experiencing the Regression on yourself. Join the class today so we can get started on this exciting adventure together!: )Astrid aka The Psychic WitchAstrid aka The Psychic Witch is an acclaimed Tarot reader, radio show host, and magician. Combining ancient knowledge with modern, edge psychology and coaching, she's creating easy-to-follow courses, offering deep knowledge that's explained without unnecessary complexity and occult bias. She has experience teaching physical classes in London, as well as many years of sharing her knowledge online. She worked as a spiritual advisor for renowned clients, such as Tower of London, Penguin Publishing House, Park Plaza Hotel Westminster, Sternberg Clarke Entertainment, and many others and her courses are continuously Bestselling on Udemy...

17. Regression with Automatic Differentiation in TensorFlow

coursera

In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow. In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

18. Simple Nearest Neighbors Regression and Classification

coursera

In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making. For instance,: Is a consumer going to default on a loan or not? Will the company make a profit? Should we extend into a certain sector of the market? Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

19. Simple Regression Analysis in Public Health

coursera

Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification...

20. Linear Regression with NumPy and Python

coursera

Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed...