Introduction
Participants will firstly get themselves prepared by learning the basic linear algebra and essential programming skills for the implementation of deep learning algorithms. Participants will experience the exciting development process of applying various AI models/platforms/tools and API to build their own AI applications. In the carefully planned lab exercises, participants can follow the step-by-step instructions to build and train the AI models. The most exciting part is to learn from various hands-on lab practices in AI and Deep Learning.
Target Audience
Management, Business Analyst, Financial Analyst, IT Practitioner
Prerequisite
To get the most out of the course, participants are expected to have some basic understanding in linear algebra and programming concepts. Having completed the course Certificate in Machine Learning and Python Workshop for Data Analytics will be helpful in the better understanding of programming APIs.
Course Contents
1. Get Prepared
- Linear algebra, probability, optimization, underlyingprinciples of deep learning
- Python programming language
2. Deep Learning Fundamentals in AI
- How deep learning works?
3. Popular AI Frameworks
- ANN (Scikit Learn), CNN / RNN / LSTM (TensorFlow), Keras Framework
4. Implementing Deep Learning Algorithms
- Build neural network and train model
5. Implementing Business Applications in AI
- Stock prediction, customer churn rate analysis, recommendation system for buyers, face recognition, etc.
6. Mobile AI applications and Tensorflow-Lite (offline access)
7. Fabricating AI Applications to cloud server
- Online access to AI cloud server
