Tensorflow is the most popular and powerful open source machine learning/deep learning framework developed by Google for everyone. Tensorflow has many powerful Machine Learning API such as Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Word Embedding, Seq2Seq, Generative Adversarial Networks (GAN), Reinforcement Learning, and Meta Learning. This course will show you how to build deep learning applications using Tensorflow.

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 analsyis using either Tensorflow or Keras.

The topics include:
  • Python progrmaming
  • Machine Leanring with Deep NN
  • Image Recognition using Convolutional NN
  • Transfer Leanring with Pretrained Models
  • Sentimental Aanalysis using Recurrent NN

Learning Outcome

By end of the course, learners will acquire the following skills and knowledge
  • Coding in Python
  • Understand the basic programming concepts such as control structure, looo, function, OOP
  • Understand the basic concepts of Machine Learning and Deep Learning
  • Understand different types of neural networks such as Convolutional Neural Network and Recurrent Neural Network
  • Use Tensorflow and Keras to program Deep Learning applications such as image recognition and sentimental analysis

Who should Attend?

  • Data Analysts
  • Data Scientists
  • Machine Learning Developers

Eligibility Criteria

  • This is a beginner course for Python and Tensorflow. Basic familiarity with IT is good enough. All the programming and machine learning concepts will be progressively introduced in the class
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information is accurate as of 17 July 2019