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Amit Yadav
In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic regression problem. By the end of this project, you will have created, trained, and evaluated a neural network model that, after the training, will be able to predict house prices with a high degree of accuracy.
Notes:
- 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/uo5g60/predicting
What's inside
Syllabus
TensorFlow Beginner: Predicting House Prices with Regression
Welcome to this project-based course on Predicting House Prices with Regression using Keras and TensorFlow. In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic regression problem. Our approach will be using a neural network to solve this problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training, will be able to accurately predict price of a house if provided with some information like the age of the house, geographical location and so on.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners in neural networks and machine learning who seek to enhance their skill sets
Provides an understanding of regression problems, which is essential for machine learning practitioners
Facilitates project-based learning, enabling individuals to apply their knowledge in a practical setting
Teaches fundamentals of neural networks using Keras and TensorFlow, widely used tools in the industry
Limited to North American learners due to regional restrictions, which may restrict accessibility for those outside the region
Save this course
Save Predicting House Prices with Regression using TensorFlow to your list so you can find it easily later:
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Reviews summary
Tensorflow regression project
Learners say this good course is a well-explained, short project using TensorFlow. It's great for beginners with some understanding of neural networks or those looking for practice with TensorFlow. Many say the instructor is also great.
Assumes some neural network knowledge
"Really good course for beginners who have some understanding of Neural Networks"
"I had so much fun learning through this guided project"
"I am new to world of ML/AI and but have been polynomial regression models for over 2 years in my research. This course was just the right applied exposure I needed in ML regression. "
Instructor is great
"The instructor is great."
"The instructor done a very good job in explaining the things he have done."
"I really liked the course and it helped me to write the code."
Suitable for beginners
"Fairly easy! Good for beginners."
"Very good Trainer (Instructor) and content also"
"Excellent course for obsolutly beginners."
Good practice for TensorFlow
"The Best project to begin with tensor flow and keras"
"Fantastic course. Would like to thank the instructor."
"Its a great way to understand how to use tensorflow practically."
Well-explained project
"Great project for freshers to begin with"
"Very good project to start NN using tensorFlow"
"Clear and detailed explanation, well designed course. "
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 Predicting House Prices with Regression using TensorFlow with these
activities:
Review Python Programming Basics
Show steps
This activity will provide a refresher on Python programming, which is a prerequisite for the course.
Browse courses on
Python
Show steps
-
Review Python syntax and data structures.
-
Practice writing simple Python programs.
Review Python basics
Show steps
Refresh your knowledge by reviewing the basic concepts of Python, such as data types, variables, and control flow. This will help you with the course's coding exercises and projects.
Browse courses on
Python
Show steps
-
Go through your notes or online resources to review the basic syntax and data types of Python.
-
Practice writing simple Python scripts to test your understanding of variables, operators, and control flow.
Review 'Neural Networks and Deep Learning' by Michael Nielsen
Show steps
This activity will provide additional context and background information on neural networks and deep learning, which are foundational concepts in the course.
Show steps
-
Read the book 'Neural Networks and Deep Learning' by Michael Nielsen.
-
Summarize key concepts and ideas from the book.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Find a mentor who can provide guidance on using Keras and TensorFlow for regression
Show steps
Help you gain access to expert advice and support in using Keras and TensorFlow for regression.
Browse courses on
Neural Networks
Show steps
-
Search for mentors in your network or online.
-
Reach out to potential mentors and ask for their guidance.
Learn about Keras and TensorFlow
Show steps
Familiarize yourself with the Keras and TensorFlow libraries by following online tutorials or reading documentation. This will help you understand the concepts of deep learning and prepare you for the course's practical exercises.
Show steps
-
Find online tutorials or documentation for Keras and TensorFlow.
-
Go through the tutorials or documentation to understand the basic concepts of deep learning, such as neural networks, activation functions, and optimization algorithms.
-
Try implementing simple examples using Keras and TensorFlow to practice your understanding.
Practice House Price Prediction on Simple Dataset
Show steps
This activity will provide hands-on experience with using regression for house price prediction and reinforce ideas from the course.
Show steps
-
Gather a simple dataset for house price prediction.
-
Train a simple Neural Network model for house price prediction.
-
Evaluate the performance of the model on the dataset.
Practice creating neural networks using Keras and TensorFlow
Show steps
Help solidify your understanding of using Keras and TensorFlow to create neural networks.
Browse courses on
Neural Networks
Show steps
-
Follow along with the course instructor's examples to create a neural network model.
-
Create your own custom neural network model using Keras and TensorFlow. Experiment with different architectures and parameters to see how they affect the performance of the model.
Explore tutorials on using Keras and TensorFlow for regression problems
Show steps
Help you gain a deeper understanding of how to use Keras and TensorFlow to solve regression problems.
Browse courses on
Neural Networks
Show steps
-
Find tutorials on using Keras and TensorFlow for regression problems.
-
Follow along with the tutorials to learn how to create and train a neural network model for regression.
Solve coding challenges
Show steps
Practice your coding skills by solving coding challenges on platforms like LeetCode or HackerRank. This will help you improve your problem-solving abilities and prepare you for the course's programming assignments.
Show steps
-
Choose a coding challenge platform and select a few problems to solve.
-
Read the problem statement carefully and try to come up with a solution.
-
Implement your solution in Python and test it against the provided test cases.
-
Review your solution and identify areas for improvement.
Solve practice problems using Keras and TensorFlow for regression
Show steps
Help you apply your knowledge of Keras and TensorFlow to solve real-world regression problems.
Browse courses on
Neural Networks
Show steps
-
Find practice problems on using Keras and TensorFlow for regression.
-
Solve the practice problems to test your understanding of how to use Keras and TensorFlow to solve regression problems.
Form a Study Group for Course Discussions
Show steps
This activity will provide learners with opportunities to engage with peers, ask questions, and reinforce their understanding of the course material.
Show steps
-
Connect with classmates and form a study group.
-
Establish regular meeting times for group discussions.
-
Review course materials together and discuss concepts.
-
Collaborate on assignments and projects (optional).
Attend a Workshop on Neural Network Applications
Show steps
This activity will provide learners with an opportunity to gain hands-on experience with neural network applications and explore advanced concepts.
Browse courses on
Neural Networks
Show steps
-
Identify and register for a relevant workshop on neural network applications.
-
Attend the workshop and actively participate in sessions.
-
Apply the knowledge and skills gained from the workshop to the course.
Build a House Price Prediction Model for a Specific Location
Show steps
This activity will allow learners to apply their knowledge and skills to a real-world problem and predict prices based on specific location data.
Show steps
-
Gather data on house prices and relevant features for a specific location.
-
Prepare and clean the data for use in a neural network model.
-
Build and train a neural network model for house price prediction using the specific location data.
-
Evaluate the performance of the model and make adjustments as needed.
-
Present the results of the project, including the model's performance and insights.
Review Python Programming Basics
Show steps
This activity will provide a refresher on Python programming, which is a prerequisite for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data structures.
- Practice writing simple Python programs.
Review Python basics
Show steps
Refresh your knowledge by reviewing the basic concepts of Python, such as data types, variables, and control flow. This will help you with the course's coding exercises and projects.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go through your notes or online resources to review the basic syntax and data types of Python.
- Practice writing simple Python scripts to test your understanding of variables, operators, and control flow.
Review 'Neural Networks and Deep Learning' by Michael Nielsen
Show steps
This activity will provide additional context and background information on neural networks and deep learning, which are foundational concepts in the course.
Show steps
Show steps
Show steps
- Read the book 'Neural Networks and Deep Learning' by Michael Nielsen.
- Summarize key concepts and ideas from the book.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Find a mentor who can provide guidance on using Keras and TensorFlow for regression
Show steps
Help you gain access to expert advice and support in using Keras and TensorFlow for regression.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Search for mentors in your network or online.
- Reach out to potential mentors and ask for their guidance.
Learn about Keras and TensorFlow
Show steps
Familiarize yourself with the Keras and TensorFlow libraries by following online tutorials or reading documentation. This will help you understand the concepts of deep learning and prepare you for the course's practical exercises.
Show steps
Show steps
Show steps
- Find online tutorials or documentation for Keras and TensorFlow.
- Go through the tutorials or documentation to understand the basic concepts of deep learning, such as neural networks, activation functions, and optimization algorithms.
- Try implementing simple examples using Keras and TensorFlow to practice your understanding.
Practice House Price Prediction on Simple Dataset
Show steps
This activity will provide hands-on experience with using regression for house price prediction and reinforce ideas from the course.
Show steps
Show steps
Show steps
- Gather a simple dataset for house price prediction.
- Train a simple Neural Network model for house price prediction.
- Evaluate the performance of the model on the dataset.
Practice creating neural networks using Keras and TensorFlow
Show steps
Help solidify your understanding of using Keras and TensorFlow to create neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Follow along with the course instructor's examples to create a neural network model.
- Create your own custom neural network model using Keras and TensorFlow. Experiment with different architectures and parameters to see how they affect the performance of the model.
Explore tutorials on using Keras and TensorFlow for regression problems
Show steps
Help you gain a deeper understanding of how to use Keras and TensorFlow to solve regression problems.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find tutorials on using Keras and TensorFlow for regression problems.
- Follow along with the tutorials to learn how to create and train a neural network model for regression.
Solve coding challenges
Show steps
Practice your coding skills by solving coding challenges on platforms like LeetCode or HackerRank. This will help you improve your problem-solving abilities and prepare you for the course's programming assignments.
Show steps
Show steps
Show steps
- Choose a coding challenge platform and select a few problems to solve.
- Read the problem statement carefully and try to come up with a solution.
- Implement your solution in Python and test it against the provided test cases.
- Review your solution and identify areas for improvement.
Solve practice problems using Keras and TensorFlow for regression
Show steps
Help you apply your knowledge of Keras and TensorFlow to solve real-world regression problems.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find practice problems on using Keras and TensorFlow for regression.
- Solve the practice problems to test your understanding of how to use Keras and TensorFlow to solve regression problems.
Form a Study Group for Course Discussions
Show steps
This activity will provide learners with opportunities to engage with peers, ask questions, and reinforce their understanding of the course material.
Show steps
Show steps
Show steps
- Connect with classmates and form a study group.
- Establish regular meeting times for group discussions.
- Review course materials together and discuss concepts.
- Collaborate on assignments and projects (optional).
Attend a Workshop on Neural Network Applications
Show steps
This activity will provide learners with an opportunity to gain hands-on experience with neural network applications and explore advanced concepts.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop on neural network applications.
- Attend the workshop and actively participate in sessions.
- Apply the knowledge and skills gained from the workshop to the course.
Build a House Price Prediction Model for a Specific Location
Show steps
This activity will allow learners to apply their knowledge and skills to a real-world problem and predict prices based on specific location data.
Show steps
Show steps
Show steps
- Gather data on house prices and relevant features for a specific location.
- Prepare and clean the data for use in a neural network model.
- Build and train a neural network model for house price prediction using the specific location data.
- Evaluate the performance of the model and make adjustments as needed.
- Present the results of the project, including the model's performance and insights.
Career center
Learners who complete Predicting House Prices with Regression using TensorFlow will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, analyzing, and interpreting large datasets to identify hidden patterns and trends. This course can be a valuable addition to a Data Scientist's skill set, as it provides hands-on experience with TensorFlow, a popular framework for building and training machine learning models. The course also covers the basics of regression modeling, which is used to predict continuous outcomes such as house prices.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models to solve real-world problems. This course can be a valuable addition to a Machine Learning Engineer's skill set, as it provides hands-on experience with TensorFlow, a popular framework for building and training machine learning models. The course also covers the basics of regression modeling, which is used to predict continuous outcomes such as house prices.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts analyze financial data to identify investment opportunities. This course can be a valuable addition to a Quantitative Analyst's skill set, as it provides hands-on experience with TensorFlow, a popular framework for building and training machine learning models. The course also covers the basics of regression modeling, which is used to predict continuous outcomes such as house prices.
For more career information including salaries, visit:
OpenCourser.com/course/uo5g60/predicting
Reading list
We've selected ten 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
Predicting House Prices with Regression using TensorFlow.
Save
A textbook often used in academic institutions, this book provides a comprehensive and in-depth coverage of deep learning. Can be used as a current reference or for supplemental reading.
Focuses on deep learning using Python, covering the fundamentals as well as advanced topics. It's a useful reference for those familiar with the basics.
Offers a practical and hands-on approach to machine learning, teaching how to use popular tools and libraries. It's particularly useful as a reference.
Presents machine learning from a probabilistic perspective, focusing on Bayesian methods. It's particularly relevant for those interested in the theoretical foundations of ML.
Provides a comprehensive introduction to machine learning, emphasizing predictive data analytics. It's suitable for those seeking a broad understanding of ML concepts.
Offers a practical approach to machine learning, with a focus on real-world applications. It's useful for gaining insights into implementing ML solutions effectively.
Covers the theoretical foundations of machine learning, making it a valuable resource for those seeking a deeper understanding of the underlying concepts.
Introduces machine learning using the R programming language, providing a practical and hands-on approach. Beneficial for those with some background in R.
Covers statistical learning methods, including sparse models. Suitable for those interested in the mathematical and theoretical aspects of machine learning.
Covers the basics of machine learning with Python, offering step-by-step examples and exercises. Useful for building foundational knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uo5g60/predicting
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Suitable for beginners in neural networks and machine learning who seek to enhance their skill sets
Provides an understanding of regression problems, which is essential for machine learning practitioners
Facilitates project-based learning, enabling individuals to apply their knowledge in a practical setting
Teaches fundamentals of neural networks using Keras and TensorFlow, widely used tools in the industry
Limited to North American learners due to regional restrictions, which may restrict accessibility for those outside the region
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