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Ikechukwu Nigel Ogbuchi
In this 1-hour long project-based course, you will learn how to build your own Machine Learning Image Classifier using Python and Colab. You will be able to easily load the data, preview it, process and normalize it, then train and test your model! I hope you enjoy the experience!
Read more
In this 1-hour long project-based course, you will learn how to build your own Machine Learning Image Classifier using Python and Colab. You will be able to easily load the data, preview it, process and normalize it, then train and test your model! I hope you enjoy the experience!
Read more
In this 1-hour long project-based course, you will learn how to build your own Machine Learning Image Classifier using Python and Colab. You will be able to easily load the data, preview it, process and normalize it, then train and test your model! I hope you enjoy the experience!
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/bd59wx/build
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What's inside
Syllabus
Project Overview
By the end of this project, you will have built your own machine learning image classifier using python with Colaboratory which allows anybody to write and execute arbitrary python code through the browser. You will learn about how to load data, visualize it and preprocess it. Then you will train and test this model on the test set and your own custom image.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for learners by introducing the basics of machine learning image classification
Develops skills in applying Python for image classification tasks
Provides hands-on training through interactive Jupyter Notebooks
Suitable for learners interested in exploring the fundamentals of machine learning image classification
Requirement to be based in the North America region may limit accessibility for some learners
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Reviews summary
Machine learning image classifier
According to learners, coding and model training are covered in this course. Students also learn about image layouts and input shaping. In particular, learners seem to appreciate that the instructor explains concepts clearly and that assignments are engaging, but some students say that the course moves too quickly.
Assignments are interesting.
"Assignments are engaging"
"I enjoy doing the assignments"
"Assignments are challenging but fair."
Instructor presents concepts clearly.
"Very clear explanations by the instructor"
"Instructor is an excellent communicator"
"I understand concepts better now thanks to the instructor's explanations."
Instructor is not involved enough.
"Lack of instructor involvement"
"Instructor is not very responsive"
"I wish the instructor was more involved."
Course moves too quickly.
"Course moves too quickly"
"I wish the course was a bit slower"
"I had to spend extra time to catch up."
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 Build a Machine Learning Image Classifier with Python with these
activities:
Create a comprehensive study guide for Machine Learning Image Classification
Show steps
Compiling a study guide will help you organize and synthesize the course materials, improving your retention and recall.
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Study Guide
Show steps
-
Gather all relevant course materials, including notes, assignments, and readings.
-
Identify key concepts and topics.
-
Summarize and organize the information in a logical manner.
Explore PyTorch Tutorials
Show steps
Following tutorials will provide you with a solid foundation in PyTorch, the deep learning library used in this course, and help you develop practical skills in building and training machine learning models.
Browse courses on
PyTorch
Show steps
-
Visit the PyTorch website and explore the beginner-friendly tutorials.
-
Complete the 'Build your first neural network' tutorial.
-
Experiment with different neural network architectures and datasets.
Image Classification Exercises
Show steps
Regular practice with image classification exercises will enhance your understanding of the concepts covered in the course and improve your ability to apply machine learning techniques to real-world image data.
Browse courses on
Image Classification
Show steps
-
Find online resources or datasets for image classification exercises.
-
Implement different image classification algorithms, such as logistic regression or support vector machines.
-
Evaluate your models' performance and experiment with different parameters.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Perform practice questions in Python and Colab
Show steps
Practicing questions in Python and Colab will provide you with hands-on experience and enhance your understanding of the concepts covered in the course.
Browse courses on
Python
Show steps
-
Identify relevant questions based on the course content.
-
Solve the questions using Python and Colab.
-
Review and analyze your solutions.
Mentor a junior learner in Machine Learning Image Classification
Show steps
Mentoring others will reinforce your understanding of the concepts and provide you with opportunities to refine your communication and teaching skills.
Browse courses on
Mentoring
Show steps
-
Identify a junior learner who would benefit from your guidance.
-
Establish regular sessions to provide support and guidance.
-
Share your knowledge and experience in Machine Learning Image Classification.
Explore additional tutorials on Machine Learning Image Classification
Show steps
Seeking out and following additional tutorials will supplement the course material and provide you with a deeper understanding of the subject.
Browse courses on
Python
Show steps
-
Identify tutorials that cover advanced concepts in Machine Learning Image Classification.
-
Follow the tutorials, implementing the techniques and algorithms.
-
Experiment with different parameters and datasets to observe the impact on model performance.
Build a Portfolio of Image Classifiers
Show steps
Creating a portfolio of image classifiers will allow you to showcase your skills, apply your knowledge to practical problems, and build a foundation for future career opportunities in machine learning.
Browse courses on
Machine Learning Projects
Show steps
-
Identify different use cases for image classifiers.
-
Collect and prepare datasets for each use case.
-
Develop and train image classification models.
-
Deploy and evaluate your models.
-
Create a portfolio website or document to showcase your projects.
Participate in online competitions and hackathons related to Machine Learning Image Classification
Show steps
Participating in competitions and hackathons will challenge you to apply your skills and knowledge in a real-world setting, fostering innovation and collaboration.
Show steps
-
Identify and register for relevant online competitions or hackathons.
-
Form or join a team with complementary skills.
-
Develop a solution that addresses the competition's objectives.
-
Submit your solution and participate in the evaluation process.
Create a visual representation of the Machine Learning Image Classification process
Show steps
Creating a visual representation will help you visualize the steps involved in Machine Learning Image Classification, solidifying your understanding.
Browse courses on
Data Visualization
Show steps
-
Choose a suitable visualization tool (e.g., diagrams, flowcharts).
-
Map out the key stages of the Machine Learning Image Classification process.
-
Annotate the visualization with relevant details and explanations.
Develop and document a custom Machine Learning Image Classifier model
Show steps
Building a custom model will provide you with a practical understanding of the entire Machine Learning Image Classification process, from data preparation to model evaluation.
Browse courses on
Model Development
Show steps
-
Define a specific use case for your custom image classifier.
-
Gather and prepare a suitable dataset.
-
Implement the Machine Learning Image Classification algorithm using a suitable library or framework.
-
Train and evaluate the model, adjusting parameters for optimal performance.
-
Document your model development process and findings.
Create a comprehensive study guide for Machine Learning Image Classification
Show steps
Compiling a study guide will help you organize and synthesize the course materials, improving your retention and recall.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather all relevant course materials, including notes, assignments, and readings.
- Identify key concepts and topics.
- Summarize and organize the information in a logical manner.
Explore PyTorch Tutorials
Show steps
Following tutorials will provide you with a solid foundation in PyTorch, the deep learning library used in this course, and help you develop practical skills in building and training machine learning models.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Visit the PyTorch website and explore the beginner-friendly tutorials.
- Complete the 'Build your first neural network' tutorial.
- Experiment with different neural network architectures and datasets.
Image Classification Exercises
Show steps
Regular practice with image classification exercises will enhance your understanding of the concepts covered in the course and improve your ability to apply machine learning techniques to real-world image data.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Find online resources or datasets for image classification exercises.
- Implement different image classification algorithms, such as logistic regression or support vector machines.
- Evaluate your models' performance and experiment with different parameters.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Perform practice questions in Python and Colab
Show steps
Practicing questions in Python and Colab will provide you with hands-on experience and enhance your understanding of the concepts covered in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify relevant questions based on the course content.
- Solve the questions using Python and Colab.
- Review and analyze your solutions.
Mentor a junior learner in Machine Learning Image Classification
Show steps
Mentoring others will reinforce your understanding of the concepts and provide you with opportunities to refine your communication and teaching skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a junior learner who would benefit from your guidance.
- Establish regular sessions to provide support and guidance.
- Share your knowledge and experience in Machine Learning Image Classification.
Explore additional tutorials on Machine Learning Image Classification
Show steps
Seeking out and following additional tutorials will supplement the course material and provide you with a deeper understanding of the subject.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify tutorials that cover advanced concepts in Machine Learning Image Classification.
- Follow the tutorials, implementing the techniques and algorithms.
- Experiment with different parameters and datasets to observe the impact on model performance.
Build a Portfolio of Image Classifiers
Show steps
Creating a portfolio of image classifiers will allow you to showcase your skills, apply your knowledge to practical problems, and build a foundation for future career opportunities in machine learning.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify different use cases for image classifiers.
- Collect and prepare datasets for each use case.
- Develop and train image classification models.
- Deploy and evaluate your models.
- Create a portfolio website or document to showcase your projects.
Participate in online competitions and hackathons related to Machine Learning Image Classification
Show steps
Participating in competitions and hackathons will challenge you to apply your skills and knowledge in a real-world setting, fostering innovation and collaboration.
Show steps
Show steps
Show steps
- Identify and register for relevant online competitions or hackathons.
- Form or join a team with complementary skills.
- Develop a solution that addresses the competition's objectives.
- Submit your solution and participate in the evaluation process.
Create a visual representation of the Machine Learning Image Classification process
Show steps
Creating a visual representation will help you visualize the steps involved in Machine Learning Image Classification, solidifying your understanding.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a suitable visualization tool (e.g., diagrams, flowcharts).
- Map out the key stages of the Machine Learning Image Classification process.
- Annotate the visualization with relevant details and explanations.
Develop and document a custom Machine Learning Image Classifier model
Show steps
Building a custom model will provide you with a practical understanding of the entire Machine Learning Image Classification process, from data preparation to model evaluation.
Browse courses on
Model Development
Show steps
Show steps
Show steps
- Define a specific use case for your custom image classifier.
- Gather and prepare a suitable dataset.
- Implement the Machine Learning Image Classification algorithm using a suitable library or framework.
- Train and evaluate the model, adjusting parameters for optimal performance.
- Document your model development process and findings.
Career center
Learners who complete Build a Machine Learning Image Classifier with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and maintenance of machine learning models. This course will give you the skills you need to build your own image classifier, a valuable tool for many different industries. Whether you are looking to enter or advance your career as a Machine Learning Engineer, this course may be useful.
Sales Manager
Sales Manager
Sales Managers use machine learning to improve their sales process. This course will teach you how to build a machine learning image classifier, a valuable tool for any Sales Manager. Whether you are looking to enter or advance your career as a Sales Manager, this course may be useful.
Data Scientist
Data Scientist
To succeed as a Data Scientist, it is vital to be able to build and use different machine learning models. This course not only teaches you how to build a machine learning image classifier, but it also provides a solid foundation in loading, visualizing, and preprocessing data. If you want to start or advance your career as a Data Scientist, this course may be useful.
Business Analyst
Business Analyst
Business Analysts use machine learning to improve business processes. This course will teach you how to build a machine learning image classifier, a valuable tool for any Business Analyst. Whether you are looking to enter or advance your career as a Business Analyst, this course may be useful.
Software Engineer
Software Engineer
Software Engineers often use machine learning to improve their applications. This course will teach you how to build a machine learning image classifier, a valuable skill for any Software Engineer. Whether you are looking to enter or advance your career as a Software Engineer, this course may be useful.
Data Analyst
Data Analyst
Data Analysts use machine learning to uncover patterns and trends in data. This course will teach you how to build a machine learning image classifier, a valuable tool for any Data Analyst. Whether you are looking to enter or advance your career as a Data Analyst, this course may be useful.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to make investment decisions. This course will teach you how to build a machine learning image classifier, a valuable tool for any Financial Analyst. Whether you are looking to enter or advance your career as a Financial Analyst, this course may be useful.
Product Manager
Product Manager
Product Managers use machine learning to improve their products. This course will teach you how to build a machine learning image classifier, a valuable tool for any Product Manager. Whether you are looking to enter or advance your career as a Product Manager, this course may be useful.
Operations Manager
Operations Manager
Operations Managers use machine learning to improve their operations. This course will teach you how to build a machine learning image classifier, a valuable tool for any Operations Manager. Whether you are looking to enter or advance your career as an Operations Manager, this course may be useful.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning to make predictions about financial markets. This course will teach you how to build a machine learning image classifier, a valuable tool for any Quantitative Analyst. Whether you are looking to enter or advance your career as a Quantitative Analyst, this course may be useful.
UX Researcher
UX Researcher
UX Researchers use machine learning to improve user experience. This course will teach you how to build a machine learning image classifier, a valuable tool for any UX Researcher. Whether you are looking to enter or advance your career as a UX Researcher, this course may be useful.
Education Researcher
Education Researcher
Education Researchers use machine learning to improve educational outcomes. This course will teach you how to build a machine learning image classifier, a valuable tool for any Education Researcher. Whether you are looking to enter or advance your career as an Education Researcher, this course may be useful.
Social Scientist
Social Scientist
Social Scientists use machine learning to understand human behavior. This course will teach you how to build a machine learning image classifier, a valuable tool for any Social Scientist. Whether you are looking to enter or advance your career as a Social Scientist, this course may be useful.
Marketing Manager
Marketing Manager
Marketing Managers use machine learning to target their marketing campaigns. This course will teach you how to build a machine learning image classifier, a valuable tool for any Marketing Manager. Whether you are looking to enter or advance your career as a Marketing Manager, this course may be useful.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use machine learning to improve patient care. This course will teach you how to build a machine learning image classifier, a valuable tool for any Healthcare Analyst. Whether you are looking to enter or advance your career as a Healthcare Analyst, this course may be useful.
For more career information including salaries, visit:
OpenCourser.com/course/bd59wx/build
Reading list
We've selected 13 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
Build a Machine Learning Image Classifier with Python.
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Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for beginners who want to learn the fundamentals of deep learning.
Provides a comprehensive introduction to machine learning with Python, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for beginners who want to learn the fundamentals of machine learning.
Provides a comprehensive introduction to machine learning for computer vision, covering topics such as image processing, feature detection, and object recognition. It valuable resource for beginners who want to learn the fundamentals of machine learning for computer vision.
Practical guide to machine learning with Python, covering topics such as data preprocessing, model selection, and evaluation. It valuable resource for beginners who want to learn how to build and deploy machine learning models.
Save
Provides a comprehensive introduction to computer vision, covering topics such as image processing, feature detection, and object recognition. It valuable resource for beginners who want to learn the fundamentals of computer vision.
Provides a comprehensive introduction to digital image processing, covering topics such as image enhancement, image restoration, and image segmentation. It valuable resource for beginners who want to learn the fundamentals of digital image processing.
Provides a comprehensive introduction to deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for beginners who want to learn the fundamentals of deep learning.
Save
Provides a comprehensive introduction to pattern recognition, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for beginners who want to learn the fundamentals of pattern recognition.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for beginners who want to learn the fundamentals of statistical learning.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for beginners who want to learn the fundamentals of pattern recognition and machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for beginners who want to learn the fundamentals of machine learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering topics such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for beginners who want to learn the fundamentals of machine learning from a probabilistic perspective.
Provides a comprehensive introduction to probabilistic graphical models, covering topics such as Bayesian networks, Markov networks, and factor graphs. It valuable resource for beginners who want to learn the fundamentals of probabilistic graphical models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bd59wx/build
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ikechukwu Nigel Ogbuchi
Language
English
Good to know
Builds a strong foundation for learners by introducing the basics of machine learning image classification
Develops skills in applying Python for image classification tasks
Provides hands-on training through interactive Jupyter Notebooks
Suitable for learners interested in exploring the fundamentals of machine learning image classification
Requirement to be based in the North America region may limit accessibility for some learners
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