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Chris Shockley
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Neural Net model to the data, and examine the results using a Confusion Matrix.
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Neural Net model to the data, and examine the results using a Confusion Matrix.
Read more
In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Neural Net model to the data, and examine the results using a Confusion Matrix.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/14z6r4/predict
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What's inside
Syllabus
Project Overview
Here you will describe what the project is about. It should give an overview of what the learner will achieve by completing this project.
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Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners in North America region with focus on this geographic area
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Reviews summary
Beginner-friendly intro to neural networks
Learners say this course is easy to follow and provides a well-rounded introduction to the basics of neural networks. Beginners especially appreciate the clear and concise teaching style and step-by-step approach. Real world examples and interactive projects contribute to a highly engaging learning experience.
Plenty of real world examples and assignments
"real world examples helped me understand the concepts better"
"interactive projects were a great way to apply what I learned"
"assignments were challenging but fair"
Course is beginner-friendly
"great intro to neural networks for beginners"
"clear and concise teaching style"
"step-by-step approach made it easy to follow"
Covers only the basics of neural networks
"just a basic overview of neural networks"
"wanted to learn more advanced techniques"
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 Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set with these
activities:
Review basic probability and statistics concepts
Show steps
Brushing up on probability and statistics can create a stronger foundation for your work in this course.
Browse courses on
Probability
Show steps
-
Study notes or textbooks on probability and statistics.
-
Complete practice problems or online quizzes.
Join a study group on Neural Networks
Show steps
Join a study group on neural networks to meet other students who are interested in the topic and to learn from each other.
Browse courses on
Neural Networks
Show steps
-
Find a study group on neural networks that you can join
-
Attend the study group meetings and participate in the discussions
Read The Elements of Statistical Learning
Show steps
Read one of the seminal textbooks in machine learning to bolster understanding of supervised and unsupervised learning techniques and how to apply them in different contexts.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read at least one chapter per week
-
Work through the exercises at the end of each chapter
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow Neural Networks tutorials
Show steps
Learn about the basics of neural networks and how they can be used to solve a variety of problems.
Browse courses on
Neural Networks
Show steps
-
Find a tutorial on neural networks that you can follow
-
Work through the tutorial and try to understand the concepts that are being taught
-
Try to apply the concepts you have learned to solve a problem of your own
Attend a workshop on neural networks
Show steps
Attending a workshop on neural networks will provide you with hands-on experience and allow you to learn from experts in the field.
Browse courses on
Neural Networks
Show steps
-
Find a workshop on neural networks that aligns with your interests.
-
Register for the workshop and attend the sessions.
Complete guided tutorials on neural networks
Show steps
Going through guided tutorials on neural networks will help you gain a better understanding of the concepts and techniques used in this course.
Browse courses on
Neural Networks
Show steps
-
Find online tutorials or courses on neural networks.
-
Follow the tutorials step-by-step and complete the exercises.
Practice coding Neural Network models
Show steps
Practice coding neural networks to improve your understanding of the concepts and to gain experience applying them to real-world problems.
Browse courses on
Neural Networks
Show steps
-
Find a dataset that you can use to train a neural network
-
Choose a neural network architecture and implement it in code
-
Train the neural network on the dataset
-
Evaluate the performance of the neural network
Attend a Neural Networks workshop
Show steps
Attend a workshop on neural networks to learn from experts and to network with other people who are interested in the field.
Browse courses on
Neural Networks
Show steps
-
Find a workshop on neural networks that you can attend
-
Register for the workshop
-
Attend the workshop and participate in the activities
Solve practice problems on neural network concepts
Show steps
Solving practice problems will help you solidify your understanding of neural network concepts and improve your problem-solving skills.
Browse courses on
Neural Networks
Show steps
-
Find practice problems online or in textbooks.
-
Solve the problems using the concepts you learn in this course.
Participate in a study group or discussion forum on neural networks
Show steps
Engaging with peers in study groups or discussion forums can help you clarify concepts, share knowledge, and gain different perspectives.
Browse courses on
Neural Networks
Show steps
-
Find a study group or discussion forum related to neural networks.
-
Participate in discussions and ask questions.
Build a simple neural network model
Show steps
Building a simple neural network model will allow you to apply the concepts you learn in this course and reinforce your understanding.
Browse courses on
Neural Networks
Show steps
-
Choose a dataset to work with.
-
Implement a neural network model in Python using libraries like TensorFlow or PyTorch.
-
Train and evaluate the model on the dataset.
Build a Neural Network project
Show steps
Build a project that uses a neural network to solve a real-world problem. This will help you to apply the concepts you have learned and to gain experience working on a larger project.
Browse courses on
Neural Networks
Show steps
-
Define the problem that you want to solve
-
Gather the data that you need to train the neural network
-
Choose a neural network architecture and implement it in code
-
Train the neural network on the data
-
Evaluate the performance of the neural network
Review basic probability and statistics concepts
Show steps
Brushing up on probability and statistics can create a stronger foundation for your work in this course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Study notes or textbooks on probability and statistics.
- Complete practice problems or online quizzes.
Join a study group on Neural Networks
Show steps
Join a study group on neural networks to meet other students who are interested in the topic and to learn from each other.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a study group on neural networks that you can join
- Attend the study group meetings and participate in the discussions
Read The Elements of Statistical Learning
Show steps
Read one of the seminal textbooks in machine learning to bolster understanding of supervised and unsupervised learning techniques and how to apply them in different contexts.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read at least one chapter per week
- Work through the exercises at the end of each chapter
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow Neural Networks tutorials
Show steps
Learn about the basics of neural networks and how they can be used to solve a variety of problems.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a tutorial on neural networks that you can follow
- Work through the tutorial and try to understand the concepts that are being taught
- Try to apply the concepts you have learned to solve a problem of your own
Attend a workshop on neural networks
Show steps
Attending a workshop on neural networks will provide you with hands-on experience and allow you to learn from experts in the field.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a workshop on neural networks that aligns with your interests.
- Register for the workshop and attend the sessions.
Complete guided tutorials on neural networks
Show steps
Going through guided tutorials on neural networks will help you gain a better understanding of the concepts and techniques used in this course.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find online tutorials or courses on neural networks.
- Follow the tutorials step-by-step and complete the exercises.
Practice coding Neural Network models
Show steps
Practice coding neural networks to improve your understanding of the concepts and to gain experience applying them to real-world problems.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a dataset that you can use to train a neural network
- Choose a neural network architecture and implement it in code
- Train the neural network on the dataset
- Evaluate the performance of the neural network
Attend a Neural Networks workshop
Show steps
Attend a workshop on neural networks to learn from experts and to network with other people who are interested in the field.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a workshop on neural networks that you can attend
- Register for the workshop
- Attend the workshop and participate in the activities
Solve practice problems on neural network concepts
Show steps
Solving practice problems will help you solidify your understanding of neural network concepts and improve your problem-solving skills.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Solve the problems using the concepts you learn in this course.
Participate in a study group or discussion forum on neural networks
Show steps
Engaging with peers in study groups or discussion forums can help you clarify concepts, share knowledge, and gain different perspectives.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a study group or discussion forum related to neural networks.
- Participate in discussions and ask questions.
Build a simple neural network model
Show steps
Building a simple neural network model will allow you to apply the concepts you learn in this course and reinforce your understanding.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Implement a neural network model in Python using libraries like TensorFlow or PyTorch.
- Train and evaluate the model on the dataset.
Build a Neural Network project
Show steps
Build a project that uses a neural network to solve a real-world problem. This will help you to apply the concepts you have learned and to gain experience working on a larger project.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Define the problem that you want to solve
- Gather the data that you need to train the neural network
- Choose a neural network architecture and implement it in code
- Train the neural network on the data
- Evaluate the performance of the neural network
Career center
Learners who complete Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their skills in programming, statistics, and machine learning to extract insights from data. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of predictive analytics.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve real-world problems. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course can help you build a foundation in the fundamentals of machine learning, including data preparation, model building, and evaluation. This course would be particularly useful for those who wish to pursue a career in the field of artificial intelligence.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis, visualization, and communication to help businesses make better decisions. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of business intelligence.
Statistician
Statistician
Statisticians use their skills in data analysis, modeling, and forecasting to solve real-world problems. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of data science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in mathematics, statistics, and programming to develop models to predict financial markets. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of financial engineering.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of data engineering.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines to support data analytics and machine learning applications. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of data infrastructure.
Business Analyst
Business Analyst
Business Analysts use their skills in data analysis, process improvement, and communication to help businesses make better decisions. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of operations research.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in financial modeling, analysis, and forecasting to help businesses make better decisions. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of corporate finance.
Actuary
Actuary
Actuaries use their skills in mathematics, statistics, and finance to assess risk and develop insurance products. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of insurance.
Risk Analyst
Risk Analyst
Risk Analysts use their skills in data analysis, modeling, and forecasting to identify and mitigate risks. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of risk management.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their skills in mathematics, modeling, and optimization to solve business problems. The Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set course would be a valuable addition to your resume, as it would demonstrate your proficiency in these skills. This course would be particularly helpful for those who wish to specialize in the field of operations management.
For more career information including salaries, visit:
OpenCourser.com/course/14z6r4/predict
Reading list
We've selected 15 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
Predict Gas Guzzlers using a Neural Net Model on the MPG Data Set.
Provides a comprehensive overview of neural networks and deep learning, including the mathematical foundations, different types of neural networks, and practical applications.
Covers practical aspects of deep learning, including how to build and train deep learning models in Python.
Provides a hands-on introduction to machine learning, covering different machine learning techniques and how to apply them using Python libraries.
Provides a comprehensive overview of statistical learning, including supervised learning, unsupervised learning, and model selection.
Provides a more advanced treatment of statistical learning, covering topics such as regularization, boosting, and support vector machines.
Provides a probabilistic perspective on machine learning, covering topics such as Bayesian inference, graphical models, and Markov chain Monte Carlo.
Save
Provides a comprehensive overview of neural networks, including different types of neural networks and their applications.
Save
Provides a comprehensive overview of deep learning, including different types of deep learning models and their applications.
Provides a more algorithmic perspective on machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of artificial intelligence, including topics such as search, planning, natural language processing, and computer vision.
Provides a practical introduction to machine learning for developers and hackers.
Provides a practical introduction to Python programming, covering topics such as data manipulation, automation, and web scraping.
Provides a practical introduction to Python for data analysis, covering topics such as data cleaning, data manipulation, and data visualization.
Save
Provides a practical introduction to R for data science, covering topics such as data cleaning, data manipulation, and data visualization.
Provides a practical introduction to machine learning with R, covering topics such as supervised learning, unsupervised learning, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/14z6r4/predict
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Effort
2 Hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Chris Shockley
Language
English
Good to know
Suitable for learners in North America region with focus on this geographic area
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