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Abhishek Kumar
As part of the TensorFlow Developer certification, this course focuses on computer vision. By the end of the course, you will know everything to build computer vision neural networks that can handle complex real-world images using TensorFlow.
Read more
As part of the TensorFlow Developer certification, this course focuses on computer vision. By the end of the course, you will know everything to build computer vision neural networks that can handle complex real-world images using TensorFlow.
Read more
As part of the TensorFlow Developer certification, this course focuses on computer vision. By the end of the course, you will know everything to build computer vision neural networks that can handle complex real-world images using TensorFlow.
Computer vision, especially Image Classification, is one of the most exciting areas of AI and Machine learning with ground-breaking real-world applications. As part of the TensorFlow Developer certification, this course focuses on image classification leveraging the TensorFlow framework. In this course, TensorFlow Developer Certificate - Image Classification, you’ll gain the ability to build, train, evaluate, and tune computer vision neural network models using the TensorFlow framework. First, you’ll explore computer vision and its application and how Convolutional Neural Networks can be built and used for image classification use cases. Next, you’ll discover techniques and TensorFlow components for handling complex and real-world images, such as ImageGenerator and image augmentation. Finally, you’ll learn how to apply transfer learning techniques to improve model performance and extend the binary classification setup to multi-class classification problems. When you’re finished with this course, you’ll have the skills and knowledge of TensorFlow components needed to build computer vision neural works for image classification.
Register for this course and see more details by visiting:
OpenCourser.com/course/zgobot/tensorflow
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What's inside
Syllabus
Course Overview
Getting Started With Computer Vision
Understanding Convolutional Neural Networks
Dealing with Real-world Images
Read more
Syllabus
Course Overview
Getting Started With Computer Vision
Understanding Convolutional Neural Networks
Dealing with Real-world Images
Read more
Applying Transfer Learning
Creating Multi-class Classification Model
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores TensorFlow, which is standard in the industry for image classification
Taught by Abhishek Kumar, who is recognized for their work in computer vision
Provides learners with hands-on labs and interactive materials
Requires prerequisites that may pose a barrier to taking this course
Suitable for learners who wish to enter the field of computer vision and machine learning
Covers complex real-world images
Teaches multi-class classification problems
Save this course
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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 TensorFlow Developer Certificate - Image Classification with these
activities:
Review your previous knowledge of computer vision techniques
Show steps
By reviewing your prior knowledge, you can refresh your memory and prepare for the course
Browse courses on
Computer Vision
Show steps
-
Read through your notes or textbooks
-
Watch online tutorials or videos
Review Convolutional Neural Networks
Show steps
Brushing up on your understanding of CNNs will help you hit the ground running when the course begins.
Browse courses on
Convolutional Neural Networks
Show steps
-
Review the basics of neural networks
-
Study the architecture of CNNs
-
Implement a simple CNN in your preferred programming language
Review Computer Vision Fundamentals
Show steps
Reviewing computer vision fundamentals will provide a solid foundation for the concepts covered in this course.
Browse courses on
Computer Vision
Show steps
-
Read introductory articles or books on computer vision.
-
Watch online tutorials or videos on computer vision concepts.
-
Complete practice exercises or quizzes on computer vision basics.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Deep Learning with Python
Show steps
Reading this book will provide you with a comprehensive understanding of deep learning concepts.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read the book
-
Take notes
-
Complete the exercises
Join a study group and discuss the course material
Show steps
Peer discussions help reinforce your understanding and provide new perspectives
Show steps
-
Find or create a study group
-
Meet regularly to discuss course content
-
Share knowledge and ask questions
TensorFlow Image Classification Tutorial
Show steps
Following this tutorial will provide you with a solid foundation in using TensorFlow for image classification.
Browse courses on
TensorFlow
Show steps
-
Set up your TensorFlow environment
-
Load and preprocess your image data
-
Build and train a CNN model
-
Evaluate the performance of your model
Practice Image Preprocessing Techniques
Show steps
Practicing image preprocessing techniques will enhance your understanding of how to prepare images for computer vision models.
Browse courses on
Image Preprocessing
Show steps
-
Implement image resizing and cropping functions using TensorFlow.
-
Apply image augmentation techniques such as flipping, rotating, and color jittering.
-
Create custom image preprocessing pipelines for specific datasets.
Follow tutorials on Convolutional Neural Networks
Show steps
Hands-on tutorials will help you solidify your understanding of CNNs for image classification
Browse courses on
Convolutional Neural Networks
Show steps
-
Find tutorials from reputable sources
-
Follow the instructions and complete the exercises
-
Experiment with different parameters and settings
Image Classification Exercises
Show steps
Practicing image classification exercises will help you develop the skills you need to build effective computer vision models.
Browse courses on
Image Classification
Show steps
-
Find a dataset of images
-
Build a CNN model to classify the images
-
Evaluate the performance of your model
-
Repeat steps 1-3 with different datasets and models
Build a Simple Image Classifier
Show steps
Building a simple image classifier will provide hands-on experience in applying the concepts learned in this course.
Browse courses on
Image Classification
Show steps
-
Define the architecture of a convolutional neural network for image classification.
-
Implement the network using TensorFlow and Keras.
-
Train and evaluate the model on a small image dataset.
-
Visualize the model's predictions and analyze its performance.
Practice image augmentation techniques
Show steps
Repetition in practicing image augmentation techniques will help you improve your skills
Browse courses on
TensorFlow
Show steps
-
Use TensorFlow's Image Augmentation API
-
Experiment with different augmentation techniques
-
Observe the impact on model performance
Contribute to an Open-Source Computer Vision Project
Show steps
Contributing to an open-source project will give you experience with real-world computer vision applications.
Browse courses on
Open Source
Show steps
-
Find a project to contribute to
-
Read the project documentation
-
Implement a new feature or fix a bug
-
Submit a pull request
Create a model for classifying images of a specific category
Show steps
Building your own model will help you apply the concepts of image classification in a practical scenario
Browse courses on
TensorFlow
Show steps
-
Choose a dataset of images
-
Train your model using TensorFlow
-
Evaluate your model's performance
Contribute to TensorFlow's computer vision library
Show steps
Contributing to open-source projects allows you to apply your skills and collaborate with others
Browse courses on
TensorFlow
Show steps
-
Find a project to contribute to
-
Read the project's documentation
-
Make a pull request with your contribution
Review your previous knowledge of computer vision techniques
Show steps
By reviewing your prior knowledge, you can refresh your memory and prepare for the course
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Read through your notes or textbooks
- Watch online tutorials or videos
Review Convolutional Neural Networks
Show steps
Brushing up on your understanding of CNNs will help you hit the ground running when the course begins.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Review the basics of neural networks
- Study the architecture of CNNs
- Implement a simple CNN in your preferred programming language
Review Computer Vision Fundamentals
Show steps
Reviewing computer vision fundamentals will provide a solid foundation for the concepts covered in this course.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Read introductory articles or books on computer vision.
- Watch online tutorials or videos on computer vision concepts.
- Complete practice exercises or quizzes on computer vision basics.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Deep Learning with Python
Show steps
Reading this book will provide you with a comprehensive understanding of deep learning concepts.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Take notes
- Complete the exercises
Join a study group and discuss the course material
Show steps
Peer discussions help reinforce your understanding and provide new perspectives
Show steps
Show steps
Show steps
- Find or create a study group
- Meet regularly to discuss course content
- Share knowledge and ask questions
TensorFlow Image Classification Tutorial
Show steps
Following this tutorial will provide you with a solid foundation in using TensorFlow for image classification.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Set up your TensorFlow environment
- Load and preprocess your image data
- Build and train a CNN model
- Evaluate the performance of your model
Practice Image Preprocessing Techniques
Show steps
Practicing image preprocessing techniques will enhance your understanding of how to prepare images for computer vision models.
Browse courses on
Image Preprocessing
Show steps
Show steps
Show steps
- Implement image resizing and cropping functions using TensorFlow.
- Apply image augmentation techniques such as flipping, rotating, and color jittering.
- Create custom image preprocessing pipelines for specific datasets.
Follow tutorials on Convolutional Neural Networks
Show steps
Hands-on tutorials will help you solidify your understanding of CNNs for image classification
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find tutorials from reputable sources
- Follow the instructions and complete the exercises
- Experiment with different parameters and settings
Image Classification Exercises
Show steps
Practicing image classification exercises will help you develop the skills you need to build effective computer vision models.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Find a dataset of images
- Build a CNN model to classify the images
- Evaluate the performance of your model
- Repeat steps 1-3 with different datasets and models
Build a Simple Image Classifier
Show steps
Building a simple image classifier will provide hands-on experience in applying the concepts learned in this course.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Define the architecture of a convolutional neural network for image classification.
- Implement the network using TensorFlow and Keras.
- Train and evaluate the model on a small image dataset.
- Visualize the model's predictions and analyze its performance.
Practice image augmentation techniques
Show steps
Repetition in practicing image augmentation techniques will help you improve your skills
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Use TensorFlow's Image Augmentation API
- Experiment with different augmentation techniques
- Observe the impact on model performance
Contribute to an Open-Source Computer Vision Project
Show steps
Contributing to an open-source project will give you experience with real-world computer vision applications.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find a project to contribute to
- Read the project documentation
- Implement a new feature or fix a bug
- Submit a pull request
Create a model for classifying images of a specific category
Show steps
Building your own model will help you apply the concepts of image classification in a practical scenario
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a dataset of images
- Train your model using TensorFlow
- Evaluate your model's performance
Contribute to TensorFlow's computer vision library
Show steps
Contributing to open-source projects allows you to apply your skills and collaborate with others
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find a project to contribute to
- Read the project's documentation
- Make a pull request with your contribution
Career center
Learners who complete TensorFlow Developer Certificate - Image Classification will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems for various applications, such as object detection, image recognition, and medical imaging. This course is highly relevant for aspiring Computer Vision Engineers as it provides a comprehensive understanding of computer vision, including image classification, convolutional neural networks, and image processing techniques. The course also covers practical aspects of dealing with real-world images and applying transfer learning.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and deploying deep learning models for various applications, including image classification, natural language processing, and speech recognition. This course can help aspiring Deep Learning Engineers build a strong foundation in computer vision and image classification, which are key areas of deep learning. The course covers techniques for handling complex and real-world images, as well as transfer learning techniques to improve model performance.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course can help aspiring Machine Learning Engineers build a strong foundation in image classification, which is a fundamental task in computer vision and machine learning. The course covers techniques for handling complex and real-world images, as well as transfer learning techniques to improve model performance.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields, including computer science, engineering, and medicine. This course may be useful for aspiring Research Scientists who are interested in specializing in computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for aspiring Software Engineers who are interested in specializing in computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI systems for a wide range of applications. This course may be useful for aspiring AI Engineers as it provides foundational knowledge in computer vision and image classification, which are essential components of many AI systems. The course covers techniques for handling complex and real-world images, as well as transfer learning techniques to improve model performance.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. This course may be useful for aspiring Product Managers who are interested in developing products that incorporate computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course may be useful for aspiring Data Analysts as it provides a foundational understanding of computer vision and image classification, which are increasingly used in data analysis for image and video analysis.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. This course may be useful for aspiring Business Analysts who are interested in using computer vision or image processing to improve business operations. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Consultant
Consultant
Consultants provide advice and support to businesses on a variety of topics, including technology, finance, and marketing. This course may be useful for aspiring Consultants who are interested in specializing in computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Data Scientist
Data Scientist
Data Scientists combine programming skills with knowledge of mathematics and statistics to extract meaningful insights from data. This course may be useful for aspiring Data Scientists as it provides a foundational understanding of computer vision and image classification, which are key techniques used in data science for image analysis and object recognition.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may be useful for aspiring Project Managers who are interested in managing projects that involve computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials to explain technical concepts to users. This course may be useful for aspiring Technical Writers who are interested in specializing in computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Sales Engineer
Sales Engineer
Sales Engineers provide technical support to customers and help them choose the right products and services. This course may be useful for aspiring Sales Engineers who are interested in selling products or services that incorporate computer vision or image processing. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns to promote products or services. This course may be useful for aspiring Marketing Managers who are interested in using computer vision or image processing to create marketing campaigns. The course provides a foundational understanding of computer vision and image classification, as well as practical techniques for handling complex and real-world images.
For more career information including salaries, visit:
OpenCourser.com/course/zgobot/tensorflow
Reading list
We've selected 14 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
TensorFlow Developer Certificate - Image Classification.
Provides a comprehensive overview of computer vision, covering fundamental concepts, algorithms, and applications. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to using deep learning for computer vision tasks. It covers a wide range of topics, including image classification, object detection, and semantic segmentation.
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning tasks. It covers a wide range of topics, including data preprocessing, model selection, and hyperparameter tuning.
Provides a comprehensive guide to deep learning using Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image processing, feature extraction, and object recognition.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Covers a wide range of machine learning topics, including image classification. It provides a practical guide to implementing machine learning algorithms in Python, which is useful for applying the concepts learned in the course.
Save
Provides a comprehensive overview of machine learning algorithms. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and machine translation.
Provides a comprehensive guide to using Python for natural language processing tasks. It covers a wide range of topics, including text processing, machine learning, and deep learning.
Provides a comprehensive overview of digital image processing techniques. It covers a wide range of topics, including image enhancement, image restoration, and image compression.
Provides a gentle introduction to machine learning with Python. While it does not focus specifically on image classification, it offers a good starting point for learners who are new to machine learning.
Save
Provides a comprehensive overview of computer graphics principles and practice. It covers a wide range of topics, including 3D modeling, rendering, and animation.
Provides a comprehensive overview of machine learning with R. While it does not focus specifically on image classification, it offers a good starting point for learners who are proficient in R and want to apply machine learning to image classification tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zgobot/tensorflow
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Time
154 hours
Level
Intermediate
Via
Pluralsight
Instructor
Abhishek Kumar
Language
English
Good to know
Explores TensorFlow, which is standard in the industry for image classification
Taught by Abhishek Kumar, who is recognized for their work in computer vision
Provides learners with hands-on labs and interactive materials
Requires prerequisites that may pose a barrier to taking this course
Suitable for learners who wish to enter the field of computer vision and machine learning
Covers complex real-world images
Teaches multi-class classification problems
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