Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Image Processing and Analysis Bootcamp With OpenCV and Deep Learning in Python
Welcome to Image Processing And Analysis in Python
Brief Introduction to the Course (2:31)
Data and Code
Get Started With the Python Data Science Environment (10:57)
For Mac Users (4:05)
Introduction to iPython/Jupyter (19:13)
Working With Colabs (7:13)
Getting Started With Basic Image Processing in Python
What Are Images? (4:54)
Read in Images in Python (7:46)
Some Basic Image Conversions (3:07)
Basic Image Resizing (4:01)
Basic Image Resizing (4:01)
What is Interpolation? A Geographic Perspective (5:08)
Basic Image Transformations (6:08)
Contrast Stretching (6:20)
Filtering Images (6:21)
Introduction to Computer Vision
What is Computer Vision? (4:54)
Read in Images Using OpenCV (5:59)
Image Filtering With OpenCV (7:30)
Edge Detection With OpenCV (5:19)
More Edge Detection: Sobel Method (3:25)
Corner Detection (1:31)
Face Detection With Haar Features: Theory (5:42)
Face Detection (5:32)
Introduction to Some Concepts
What is Machine Learning? (5:32)
Unsupervised Learning Methods
What is Unsupervised Learning? (1:38)
Theory Behind PCA (2:37)
Implement PCA on Images (4:36)
PCA For Image reconstruction (4:14)
Randomised PCA (2:45)
Theory Behind K-means (1:57)
K-Means For Image Reconstruction (1:48)
Classify High Dimensional Data With t-SNE (4:55)
Practical Case Study: Identify Flowers (3:09)
Cluster the Flowers: Read in Images (7:45)
Implement PCA (4:04)
Implement t-SNE (2:25)
Supervised Learning: Classifying Images
Brief Introduction to Supervised Learning (10:10)
Implement SVM to Classify Digits (7:00)
Accuracy Assessment (9:42)
rf (4:19)
Start With Deep Learning
Why Deep Learning? (9:51)
Tensorflow Installation (15:12)
Written Tensorflow Installation Instructions
Install Keras on Windows 10 (5:16)
Install Keras on Mac (4:19)
Written Keras Installation Instructions
Deep Learning For Image Classification
Introduction to CNN (11:25)
Implement a CNN for Multi-Class Supervised Classification (7:27)
More on CNN (4:36)
Pre-Requisite For Working With Imagery Data (2:33)
CNN on Image Data-Part 1 (10:41)
CNN on Image Data-Part 2 (6:38)
More on TFLearn (7:54)
CNN Workflow for Keras (4:04)
CNN With Keras (4:10)
CNN on Image Data with Keras-Part 1 (2:27)
CNN on Image Data with Keras-Part 2 (5:05)
Transfer Learning
What is Transfer Learning? (7:41)
Implement a Pre-Built Transfer Learning Model (6:57)
Unsupervised Deep Learning
Simple Autoencoders (5:43)
Add Sparsity Constraint (4:32)
Image Filtering With OpenCV
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock