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Welcome to the course Master Regression and Feedforward Networks.

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Welcome to the course Master Regression and Feedforward Networks.

This course will teach you to master Regression and Prediction with a large number of advanced Regression techniques for purposes of Prediction and Machine Learning Automatic Model Creation, so-called true machine intelligence or AI.

You will learn to handle advanced model structures for prediction tasks, and you will learn modeling theory and several useful ways to prepare a dataset for Data Analysis with Regression Models.

You will learn to:

  • Master Regression and Prediction both in theory and practice

  • Master Regression models from simple linear Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models

  • Use Machine Learning Automatic Model Creation and Feature Selection

  • Use Regularization of Regression models and to regularize regression models with Lasso and Ridge Regression

  • Use Decision Tree, Random Forest, and Voting Regression models

  • Use Feedforward Multilayer Networks and Advanced Regression model Structures

  • Use effective advanced Residual analysis and tools to judge models’ goodness-of-fit plus residual distributions.

  • Use the Statsmodels and Scikit-learn libraries for Regression supported by Matplotlib, Seaborn, Pandas, and Python

  • Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources.

  • Option: To use the Anaconda Distribution (for Windows, Mac, Linux)

  • Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.

  • And much more…

This course is an excellent way to learn to master Regression and Prediction.

Regression and Prediction are the most important and commonly used tools for modeling, prediction, AI, and forecasting.

This course is designed for everyone who wants to

  • learn to master Regression and Prediction

  • learn about Automatic Model Creation

  • learn advanced Data Science and Machine Learning plus improve their capabilities and productivity

Requirements:

  • Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended

  • Access to a computer with an internet connection

  • The course only uses costless software

  • Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

  • Some Python and Pandas skills are necessary. If you lack these, the course "Master Regression and Prediction with Pandas and Python" includes all knowledge you need.

This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression and Prediction.

Enroll now to receive 10+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course.

Enroll now

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Highly relevant in the field of machine learning and data analysis
Taught by instructors recognized for their expertise in the field of regression and prediction
Covers advanced regression techniques and machine learning models for accurate prediction and forecasting
Provides opportunities to enhance data science and machine learning capabilities
Utilizes popular Python libraries for regression analysis, such as Statsmodels and Scikit-learn
Offers cloud computing options to facilitate resource-intensive tasks

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Regression and Feedforward Networks [2024] with these activities:
Understand Regression
Regression Analysis is the foundation you'll use throughout the course. Completing this activity will make your learning in the course much smoother.
Browse courses on Regression Analysis
Show steps
  • Review objectives of regression analysis and when it is a suitable predictive tool
  • Understand different types of regression analysis
  • Select independent variables that are important for model building
  • Interpret the statistical results of a regression analysis
Read Elements of Statistical Learning
This book provides a comprehensive overview of statistical learning methods, including regression analysis. Reading this book will give you a strong foundation for the course.
Show steps
  • Read chapters 1-3
  • Read chapters 7-9
Solve regression problems
Practicing regression problems will help you master the techniques you learn in the course.
Browse courses on Regression Analysis
Show steps
  • Find a dataset
  • Clean the data
  • Exploratory data analysis
  • Train a regression model
  • Evaluate the model
Three other activities
Expand to see all activities and additional details
Show all six activities
Kaggle Regression Competition
Participating in a Kaggle competition gives you an opportunity to apply the skills you learned in the course to a real-world problem and compare your performance with others
Browse courses on Regression Analysis
Show steps
  • Find a regression competition
  • Download the data
  • Clean the data
  • Train a regression model
  • Submit your results
Write a paper on a regression topic
Writing a paper will help you organize and improve your understanding of the techniques you learned in the course.
Browse courses on Regression Analysis
Show steps
  • Choose a topic
  • Research the topic
  • Write an outline
  • Draft the paper
  • Revise and edit the paper
Help others learn regression
Mentoring others will reinforce your understanding of regression analysis and help you develop your communication and leadership skills.
Browse courses on Regression Analysis
Show steps
  • Find a mentee
  • Create a plan for mentoring
  • Meet regularly
  • Provide feedback
  • Celebrate successes

Career center

Learners who complete Master Regression and Feedforward Networks [2024] will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist is responsible for collecting, analyzing, and interpreting data to extract meaningful insights that can be used to inform decision-making. This course provides a strong foundation in regression and prediction techniques, which are essential for data scientists to master in order to build accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Data Scientist.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in regression and prediction techniques, which are essential for Quantitative Analysts to master in order to develop accurate and profitable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Quantitative Analyst.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a strong foundation in regression and prediction techniques, which are essential for Machine Learning Engineers to master in order to build accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Machine Learning Engineer.
Statistician
Statisticians collect, analyze, and interpret data to extract meaningful insights that can be used to inform decision-making. This course provides a strong foundation in regression and prediction techniques, which are essential for Statisticians to master in order to build accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Statistician.
Data Analyst
Data Analysts collect, analyze, and interpret data to extract meaningful insights that can be used to inform decision-making. This course provides a strong foundation in regression and prediction techniques, which are essential for Data Analysts to master in order to build accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Data Analyst.
Business Analyst
Business Analysts use data to identify and solve business problems. This course provides a strong foundation in regression and prediction techniques, which are essential for Business Analysts to master in order to develop accurate and actionable insights. By taking this course, you will gain the skills and knowledge necessary to succeed as a Business Analyst.
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course provides a strong foundation in regression and prediction techniques, which are essential for Market Researchers to master in order to develop accurate and actionable insights. By taking this course, you will gain the skills and knowledge necessary to succeed as a Market Researcher.
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment decisions. This course provides a strong foundation in regression and prediction techniques, which are essential for Financial Analysts to master in order to develop accurate and profitable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Financial Analyst.
Risk Analyst
Risk Analysts use data to identify and assess risks. This course provides a strong foundation in regression and prediction techniques, which are essential for Risk Analysts to master in order to develop accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Risk Analyst.
Actuary
Actuaries use data to assess and manage financial risks. This course provides a strong foundation in regression and prediction techniques, which are essential for Actuaries to master in order to develop accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as an Actuary.
Epidemiologist
Epidemiologists use data to study the distribution and determinants of health-related states or events in specified populations. This course provides a strong foundation in regression and prediction techniques, which are essential for Epidemiologists to master in order to develop accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as an Epidemiologist.
Biostatistician
Biostatisticians use data to design and analyze studies to answer questions about the effectiveness of medical treatments and interventions. This course provides a strong foundation in regression and prediction techniques, which are essential for Biostatisticians to master in order to develop accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as a Biostatistician.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of organizations. This course provides a strong foundation in regression and prediction techniques, which are essential for Operations Research Analysts to master in order to develop accurate and reliable models. By taking this course, you will gain the skills and knowledge necessary to succeed as an Operations Research Analyst.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers who want to develop data-driven applications. By taking this course, you will gain the skills and knowledge necessary to build accurate and reliable models that can be used to improve the performance of software applications.
Computer Scientist
Computer Scientists research and develop new computing technologies. This course may be useful for Computer Scientists who want to develop data-driven applications. By taking this course, you will gain the skills and knowledge necessary to build accurate and reliable models that can be used to improve the performance of computing technologies.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Master Regression and Feedforward Networks [2024].
Classic text on generalized linear models, which are a powerful class of models for regression and prediction. It is written in a clear and concise style, with plenty of examples and exercises.
More advanced treatment of neural networks and deep learning, with a focus on regression and prediction. It valuable reference for researchers and practitioners in the field.
This textbook provides a comprehensive introduction to Bayesian data analysis, which powerful approach to statistical modeling. It valuable resource for students and researchers in the field.
Comprehensive introduction to causal inference, which fundamental concept in statistics. It valuable resource for students and researchers in the field.
Comprehensive introduction to econometrics, which branch of statistics that is used to analyze economic data. It valuable resource for students and researchers in the field.
Comprehensive introduction to time series analysis, which branch of statistics that is used to analyze data that is collected over time. It valuable resource for students and researchers in the field.
This textbook provides a comprehensive introduction to multiple time series analysis, which branch of statistics that is used to analyze data that is collected over time. It valuable resource for students and researchers in the field.
This textbook provides a comprehensive introduction to statistical learning methods, including regression and prediction. It is written in a clear and concise style, with plenty of examples and exercises.
This textbook provides a comprehensive introduction to statistical methods for data analysis, with a focus on regression and prediction. It valuable resource for students and researchers in the field.
Provides a comprehensive overview of regression modeling, with a focus on actuarial and financial applications. It is written in a clear and concise style, with plenty of examples and exercises.
This textbook provides a comprehensive introduction to machine learning, with a focus on probabilistic models. It valuable resource for students and researchers in the field.

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