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Image Recognition with Machine Learning
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Gain insights into image data processing and CNNs for image classification using TensorFlow. Delve into CNN architectures and their applications, requiring Python and TensorFlow knowledge.

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Overview

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If you want to dive into the technology behind computer vision and self driving cars, this is where to start. In this course you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. After completing this course, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks. The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

If you want to dive into the technology behind computer vision and self driving cars, this is where to start. In this course yo...Show More

Content

45 Lessons4 Quizzes27 Challenges

What you'll learn from this course

1 Lessons

Get familiar with image recognition using CNN, including data preparation and augmentation.

Overview

Image Processing

9 Lessons

Walk through image processing techniques for model preparation using TensorFlow and Pillow.

CNN

11 Lessons

Master the steps to recognize handwritten digits using convolutional neural networks.

SqueezeNet

11 Lessons

Enhance your skills in developing efficient, high-performance image recognition models with SqueezeNet.

ResNet

13 Lessons

Deepen your knowledge of ResNet architecture, focusing on residual learning for enhanced deep network performance.

Full Model Architecture

Quiz

Course Conclusion

Certificate of Completion

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Course Author:

Adaptilab

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