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Ryan Ahmed
In this hands-on guided project, we will predict real estate prices with deep learning. In this project, we will predict home sale prices in King County in the U.S. between May, 2014 and May, 2015 using several features such as number of bedrooms, bathrooms, view, and square footage. This guided project is practical and directly applicable to the real estate industry. You can add this project to your portfolio of projects which is essential for your next job interview.
Register for this course and see more details by visiting:
OpenCourser.com/course/nulccg/deep
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What's inside
Syllabus
Project Overview
In this hands-on guided project, we will predict real estate prices with deep learning. In this project, we will predict home sale prices in King County in the U.S. between May, 2014 and May, 2015 using several features such as number of bedrooms, bathrooms, view, and square footage.
This guided project is practical and directly applicable to the real estate industry. You can add this project to your portfolio of projects which is essential for your next job interview.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Promotes methods of applying machine learning to the real estate industry
Directly applicable to real estate professions
Designed to be added to a portfolio, which may be useful for job interviews
May require prior experience in real estate and machine learning
Save this course
Save Deep Learning for Real Estate Price Prediction to your list so you can find it easily later:
Save
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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 Deep Learning for Real Estate Price Prediction with these
activities:
Organize and Review Course Materials
Show steps
Enhance knowledge retention and understanding by organizing and reviewing course materials.
Show steps
-
Create a system for organizing notes, assignments, and resources.
-
Regularly review and consolidate materials to strengthen understanding.
Get Started with Keras
Show steps
Solidify the basics of using Keras for a deep learning project.
Show steps
-
Review the Keras documentation for basic operations such as creating a model, compiling it, and training it.
-
Follow a tutorial on building a simple neural network using Keras.
Build a Simple Real Estate Prediction Model
Show steps
Put your deep learning skills to the test with a hands-on project that simulates the real-world application of the techniques taught in the course.
Show steps
-
Understand the problem statement by studying the housing data and business requirements.
-
Design the architecture of your prediction model.
-
Train your model on the housing data.
-
Evaluate the performance of your model on a hold-out dataset.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Practice Problems on Deep Learning Concepts
Show steps
Reinforce understanding of deep learning concepts and techniques.
Show steps
-
Access online platforms or textbooks that provide practice problems on deep learning.
-
Attempt to solve a set of practice problems, covering topics like data preprocessing, model training, and evaluation.
Discuss Real-World Applications of Deep Learning in Real Estate
Show steps
Strengthen understanding of how deep learning is used in the real estate industry.
Show steps
-
Form a study group with other learners taking the course.
-
Discuss case studies or articles on the application of deep learning in the real estate sector.
Create a Presentation on Deep Learning for Real Estate
Show steps
Demonstrate understanding of the concepts and applications of deep learning in real estate.
Show steps
-
Organize the main points of deep learning and its applications in the real estate industry.
-
Design visual aids such as slides, charts, or simulations to illustrate the concepts.
-
Practice delivering the presentation to gain confidence and clarity.
Explore Advanced Deep Learning Techniques for Real Estate
Show steps
Gain exposure to cutting-edge deep learning techniques applicable to the real estate domain.
Show steps
-
Identify advanced deep learning algorithms relevant to real estate, such as transformer models or generative adversarial networks.
-
Follow online tutorials or research papers to understand the implementation and application of these techniques.
Contribute to Open-Source Projects Related to Real Estate
Show steps
Gain practical experience and collaborate with others in advancing the field of deep learning for real estate.
Show steps
-
Identify open-source projects on platforms like GitHub that focus on real estate or deep learning.
-
Review the project documentation and codebase to understand its goals and implementation.
-
Make meaningful contributions to the project, such as adding features, fixing bugs, or improving documentation.
Organize and Review Course Materials
Show steps
Enhance knowledge retention and understanding by organizing and reviewing course materials.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and resources.
- Regularly review and consolidate materials to strengthen understanding.
Get Started with Keras
Show steps
Solidify the basics of using Keras for a deep learning project.
Show steps
Show steps
Show steps
- Review the Keras documentation for basic operations such as creating a model, compiling it, and training it.
- Follow a tutorial on building a simple neural network using Keras.
Build a Simple Real Estate Prediction Model
Show steps
Put your deep learning skills to the test with a hands-on project that simulates the real-world application of the techniques taught in the course.
Show steps
Show steps
Show steps
- Understand the problem statement by studying the housing data and business requirements.
- Design the architecture of your prediction model.
- Train your model on the housing data.
- Evaluate the performance of your model on a hold-out dataset.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Practice Problems on Deep Learning Concepts
Show steps
Reinforce understanding of deep learning concepts and techniques.
Show steps
Show steps
Show steps
- Access online platforms or textbooks that provide practice problems on deep learning.
- Attempt to solve a set of practice problems, covering topics like data preprocessing, model training, and evaluation.
Discuss Real-World Applications of Deep Learning in Real Estate
Show steps
Strengthen understanding of how deep learning is used in the real estate industry.
Show steps
Show steps
Show steps
- Form a study group with other learners taking the course.
- Discuss case studies or articles on the application of deep learning in the real estate sector.
Create a Presentation on Deep Learning for Real Estate
Show steps
Demonstrate understanding of the concepts and applications of deep learning in real estate.
Show steps
Show steps
Show steps
- Organize the main points of deep learning and its applications in the real estate industry.
- Design visual aids such as slides, charts, or simulations to illustrate the concepts.
- Practice delivering the presentation to gain confidence and clarity.
Explore Advanced Deep Learning Techniques for Real Estate
Show steps
Gain exposure to cutting-edge deep learning techniques applicable to the real estate domain.
Show steps
Show steps
Show steps
- Identify advanced deep learning algorithms relevant to real estate, such as transformer models or generative adversarial networks.
- Follow online tutorials or research papers to understand the implementation and application of these techniques.
Contribute to Open-Source Projects Related to Real Estate
Show steps
Gain practical experience and collaborate with others in advancing the field of deep learning for real estate.
Show steps
Show steps
Show steps
- Identify open-source projects on platforms like GitHub that focus on real estate or deep learning.
- Review the project documentation and codebase to understand its goals and implementation.
- Make meaningful contributions to the project, such as adding features, fixing bugs, or improving documentation.
Career center
Learners who complete Deep Learning for Real Estate Price Prediction will develop knowledge and skills
that may be useful to these careers:
Real Estate Appraiser
Real Estate Appraiser
Real Estate Appraisers determine the value of real property. Deep Learning for Real Estate Price Prediction can provide you with advanced skills in analyzing real estate data, which can help you make more accurate and informed property valuations.
Real Estate Analyst
Real Estate Analyst
Real Estate Analysts provide analysis and advice on real estate markets and investments. Deep Learning for Real Estate Price Prediction can provide you with valuable insights into how machine learning is being used to analyze real estate data, which can enhance your ability to make informed decisions and provide valuable insights to clients.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze and predict financial data. Real estate investment is a major asset class, and being able to analyze real estate data using machine learning techniques, such as those taught in Deep Learning for Real Estate Price Prediction, can be a valuable skill for Quantitative Analysts.
Investment Manager
Investment Manager
Investment Managers are responsible for managing investment portfolios for individuals and institutions. Real estate is often a major asset class in investment portfolios, and Deep Learning for Real Estate Price Prediction can help you develop the skills needed to analyze real estate data and make more informed investment decisions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. Deep Learning for Real Estate Price Prediction can enhance your ability to analyze data using machine learning techniques, which can help you make more informed decisions and provide valuable insights to clients or stakeholders.
Mortgage Underwriter
Mortgage Underwriter
Mortgage Underwriters evaluate the creditworthiness of potential borrowers and assess the risk of a mortgage loan. Deep Learning for Real Estate Price Prediction can provide you with a better understanding of how machine learning is used to analyze real estate data, which can help you make more informed decisions about mortgage approvals.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are in high demand due to the rise of AI-driven products and services. You would be responsible for developing, deploying, and maintaining machine learning models, including those used for predicting real estate prices. Deep Learning for Real Estate Price Prediction can help you gain practical experience in developing and deploying a deep learning model for real-world applications.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research and gather data on consumer markets, industries, and trends. Deep Learning for Real Estate Price Prediction can provide you with valuable skills in analyzing real estate data, which can help you conduct more in-depth market research and provide valuable insights to clients.
Real Estate Developer
Real Estate Developer
Real Estate Developers acquire, develop, and manage real estate. Deep Learning for Real Estate Price Prediction can provide you with a better understanding of how machine learning can be used to analyze and predict real estate market trends, which can help you make more informed decisions about land acquisition, development, and management.
Real Estate Agent
Real Estate Agent
Real Estate Agents represent buyers and sellers in real estate transactions. Deep Learning for Real Estate Price Prediction can provide you with valuable insights into how machine learning is being used to analyze real estate data, which can enhance your ability to provide informed advice to clients and help them make better real estate decisions.
Property Manager
Property Manager
Property Managers oversee the day-to-day operations of residential and commercial properties. Deep Learning for Real Estate Price Prediction can help you develop a better understanding of how machine learning can be used to analyze real estate data, which can help you make more informed decisions about property management and maintenance.
Real Estate Broker
Real Estate Broker
Real Estate Brokers facilitate the buying and selling of real estate properties. Deep Learning for Real Estate Price Prediction can provide you with valuable insights into how machine learning is being used to analyze real estate data, which can enhance your ability to provide informed advice to clients and help them make better real estate decisions.
Urban Planner
Urban Planner
Urban Planners develop plans and policies for the use and development of land in urban areas. Deep Learning for Real Estate Price Prediction can provide you with a better understanding of how machine learning can be used to analyze real estate data, which can help you make more informed decisions about land use planning and development.
Construction Manager
Construction Manager
Construction Managers oversee the planning, design, and construction of buildings and other structures. Deep Learning for Real Estate Price Prediction can provide you with insights into how machine learning can be used to analyze real estate data, which can help you make more informed decisions about project planning and management.
Data Scientist
Data Scientist
A Data Scientist is an expert in extracting and interpreting large data sets. You would use inferential and predictive analytics, as well as machine learning and artificial intelligence, to make conclusions and predictions. Deep Learning for Real Estate Price Prediction may be useful in helping you build a foundation for working with deep learning algorithms, which are increasingly used in analyzing data for real estate purposes.
For more career information including salaries, visit:
OpenCourser.com/course/nulccg/deep
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
Deep Learning for Real Estate Price Prediction.
This textbook provides comprehensive coverage of real estate finance and investments, including topics such as property valuation, mortgage financing, and investment analysis. It valuable resource for students and professionals in the real estate industry.
This classic book provides a comprehensive guide to security analysis, covering topics such as financial statement analysis, valuation, and investment strategies. It valuable resource for students and professionals who want to learn about the principles of fundamental analysis.
This classic book provides timeless advice on investing in stocks and bonds. It valuable resource for students and professionals who want to learn about the principles of value investing.
Provides a practical guide to predictive analytics, with a focus on real-world applications. It covers topics such as data preparation, model building, and model evaluation. It valuable resource for students and professionals who want to use predictive analytics to solve business problems.
Can act as a useful background to deep learning for real estate price prediction.
Provides an introduction to machine learning for real estate, covering topics such as data preparation, model building, and model evaluation. It valuable resource for students and professionals who want to use machine learning to solve real estate problems.
Can provide depth to topics related to real estate price prediction with deep learning.
Provides a broad overview of machine learning with a particular focus on use of Python.
Can be a useful resource for those interested in learning about machine learning.
Save
Provides a solid foundation in predictive modeling and can serve as background reading.
Can provide a broad overview of rea estate development, which may be helpful for understanding real estate markets.
Can provide some depth to the topic of machine learning in finance, which may be of interest to those in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nulccg/deep
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Promotes methods of applying machine learning to the real estate industry
Directly applicable to real estate professions
Designed to be added to a portfolio, which may be useful for job interviews
May require prior experience in real estate and machine learning
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