MP4 | Video: AVC 916 x 514 | Audio: AAC 44 Khz 2ch | Duration: 01:48:56 | 286.64 MB
Genre: eLearning | Language: English

The following seven topics will be covered in this Data Science and Machine Learning course:

Building a Small CNN to Classify Handwritten Digits. Build a small Convolutional Neural Network (CNN) to classify handwritten digits in this first topic in the Data Science and Machine Learning Series. Follow along with Advait and work with the MNIST Handwritten Digits Dataset.
Training the CNN Model in Keras. Train the Convolutional Neural Network (CNN) Model in Keras in this second topic in the Data Science and Machine Learning Series.
The Practical Aspects of Image Data Augmentation. Become proficient with the practical aspects of image data augmentation in this third topic in the Data Science and Machine Learning Series.
Introducing Transfer Learning. Master transfer learning in this fourth topic in the Data Science and Machine Learning Series.
Implementing Transfer Learning in Keras. Implement transfer learning in Keras in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and practice using different models for prediction and feature extraction including Xception, VGG16, VGG19, ResNet, ResNetV2, MobileNet, DenseNet, and NasNet.
Feature Extraction and Fine Tuning. Use both feature extraction and fine tuning and know when to use each approach in this sixth topic in the Data Science and Machine Learning Series. Follow along with Advait and apply feature extracting and fine tuning in four different scenarios.
Implementing Feature Extraction and Transfer Learning using ResNet-50 Base. Implement feature extraction and transfer learning using ResNet-50 Base in this seventh topic in the Data Science and Machine Learning Series.
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