Share Description
In this course, you will :
- Learn how to pass Google's TensorFlow Developer Certificate exam (and put it on your CV).
- TensorFlow models can be created utilising computer vision, convolutional neural networks, and natural language processing.
- All interactive notebooks and course slides are available as downloadable manuals.
- Improve your skills in Machine Learning and Deep Learning before taking the TensorFlow assessment exam.
- Discover how to incorporate Machine Learning into tools and apps.
- Learn how to create various sorts of Machine Learning models using the latest TensorFlow 2.
- Deep neural networks and convolutional neural networks can be used to create image recognition and text recognition algorithms.
- Using real-world photos to visualise an image's path via convolutions in order to comprehend how a computer "sees" information, plot loss, and accuracy.
- Deep Learning is being used to forecast time series.
- Learn all you need to know to become a TensorFlow Certified Developer.
- Become known as a top candidate among recruiters looking for TensorFlow developers.
Syllabus :
- Deep Learning and TensorFlow Fundamentals
- Neural network regression with TensorFlow
- Neural network classification in TensorFlow
- Computer Vision and Convolutional Neural Networks in TensorFlow
- Transfer Learning in TensorFlow Part 1: Feature extraction
- Transfer Learning in TensorFlow Part 2: Fine tuning
- Transfer Learning with TensorFlow Part 3: Scaling Up
- Milestone Project 1: Food Vision Big™
- NLP Fundamentals in TensorFlow
- Milestone Project 2: SkimLit
- Time Series fundamentals in TensorFlow + Milestone Project 3: BitPredict
- Passing the TensorFlow Developer Certificate Exam
