More Information
Special Product No
Course feature Lifetime Access, 24x7 Support, Real-time code analysis and feedback, 100% Money Back Guarantee, Certified Trainer
Funding
Category Type Training course and certification
Organisation- sponsored Non SMEs Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
SMEs Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above)
Self-Sponsored Professionals Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above) Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Students and/or Full-Time National Service (NSF) Up to 100% of the nett payable course and certification fees, capped at $2,500 per trainee
  • Lifetime Access

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

  • Certified Trainer

Learn how to write distributed machine learning models that scale in Tensorflow 2.x, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud.

Tensorflow is based on the Python, the most popular programming language for data analytics and engineering in the world.

In this course, you will equip yourself the basic and advanced knowledge of Python. After that, you will learn the basic and advanced topics in Tensorflow.

By the completion of this course, you will be able to develop your own NN, CNN and RNN model for image recognition and sentimental analysis using either Tensorflow or Keras.

Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML

• How ML optimization works and how various hyperparameters affect models during optimization

• How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform

•Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep

Category Type Training course and certification
Organisation- sponsored Non SMEs Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
SMEs Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above)
Self-Sponsored Professionals Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above) Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Students and/or Full-Time National Service (NSF) Up to 100% of the nett payable course and certification fees, capped at $2,500 per trainee

Test takers should be comfortable with:

  • Foundational principles of ML and Deep Learning

  • Building ML models in TensorFlow 2.x

  • Building image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks

  • Using real-world images in different shapes and sizes to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy

  • Exploring strategies to prevent overfitting, including augmentation and dropouts

  • Applying neural networks to solve natural language processing problems using TensorFlow

  • NSF or Full Time Students

  • Data Analysts

  • Machine Learning Engineers and Developers

  • You must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.

  • You should be a good programmer. Ideally, you should have some experience programming in Python because the programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercises anyway.

Course Outline

Course Highlights

  • Python programming

  • Machine Learning with Deep NN

  • Image Recognition using Convolutional NN

  • Transfer Leanring with Pretrained Models

  • Sentimental Analysis using Recurrent NN