Artificial Intelligence 101


Artificial intelligence is quickly becoming a fundamental building block of business operations, providing real gains such as improved processes, increased efficiency, and accelerated innovation. Advances in machine learning technology have combined with high-performance compute options and an abundance of data to create a perfect storm for AI to transform organizations of all sizes.

Early adopters are weaving AI across the organization to address business priorities. Some machine learning projects tackle incremental gains to automate processes to create efficiencies. Others are transformational initiatives aimed at innovation and competitive differentiation. While there are numerous applications of AI, enterprises already realize great value from use cases that provide new experiences for their customers and drive business growth.

Programme Highlights

  • Big Data & Artificial Intelligence Technology
  • The principles of AI algorithms and machine learning methodology
  • AI Technology including ANN, RNN, LSTM
  • Image recognition, speech recognition, textual analytics, trend prediction, robotic control, Fintech etc.

Who Should Attend?

  • Who is interested in understanding the applications and principles of AI and strategy.
  • Who intend to plan and utilize AI in their projects.
  • IT PSoftware Developers and System integrators who can learn practically how algorithms and AI basics from the ground up.


Date : 28 & 29 September 2020 (Monday &Tuesday)
Time: 09:30 – 17:00
Duration: Total 24 lecture hours

Course Fee


Programme Structure

Chapter 1 : Introduction to Big Data Analytics

  • What is Big Data Infrastructure
  • What is Big Data Analytics
  • Trends & History
  • Big Data 4Vs
  • Framework
  • Concepts of Unstructured Data

Chapter 2 : Introduction to Artificial Intelligence

  • Introduction to AI & History
  • Examples in Deep Learning
  • AI Applications in Computer Visions
  • AI Applications in Face and Gesture Recognition
  • AI Applications in Object Detection
  • AI Applications in Activity Recognition
  • AI Applications in Image Generation
  • AI Applications in Robotic Controls
  • AI Natural Language Processing
  • GPU and Hardware in AI

Chapter 3 : Introduction to Deep Learning

  • Global Deep Learning Trends
  • Machine Learning Process
  • Supervised & Unsupervised Learning
  • Reinforcement Learning
  • Classification Application Type
  • Regression Application Type
  • Clustering Application Type
  • Error Rate & Accuracy
  • Concepts of Feature Engineering
  • Revolution of Depth Layers

Chapter 4 : From Regression

  • Machine Learning Basics
  • Training, Test & Validation Process
  • Over-fitting & Under-fitting
  • From the Ground Up: Regression
  • Linear Regression
  • Logistic Regression
  • Principles in Perceptron
  • Impact of Data Cleansing and Data Transformation
  • Impact of Feature Engineering

Chapter 5 : Neural Network Basics, ANN, CNN

  • Neural Network Basics
  • Neurons
  • Activation Functions
  • Optimization & Loss
  • Activation Functions
  • Neural Network Layers
  • Artificial Neural Networks
  • Principles in Gradient Descent
  • Neural Network Weight Update Mechanisms
  • Basic Structures in Artificial Neural Network
  • 2D & 3D Convolution Operation
  • Convolutional Neurons
  • Feature Maps
  • Convolution Neural Networks
  • Word2Vec Embedding

Chapter 6 : Recurrent Neural Basics (RNN, LSTM)

  • Introduction to Recurrent Neural Network
  • Unrolling RNN Cells
  • Gates in Memory Cells
  • Memory Concepts in AI Models
  • Long-Short-Term-Memory Cells
  • Why GPU Hardware is significant
  • Applications in Speech Recognition

Chapter 7 : Recurrent Neural Network in Text Analytics

  • Textual Feature Engineering
  • Features: TF-IDF, N-grams
  • Application in Handwritten Sequence Generation
  • Application in Reading Comprehension
  • Application in Part-of-Speech Recognition
  • Application in Text Summarization
  • Application in Co-reference Resolution
  • Application in Time Series Analysis
  • Applications in Textual Analytics
  • Brief Introduction to BERT Models for AI Text

Chapter 8 : Goals for AI innovation

  • Enable product and service innovation
  • Drive research and discovery
  • Enhance the customer experience
  • Improve customer service
  • Improve healthcare outcomes
  • Increase efficiency and productivity
  • Improve security and compliance
  • Optimize supply chain operations
  • Improve decision making

Certificate & Award

Participants who have completed 100% attendance will be awarded a certificate of attendance issued by The Institute of Artificial Intelligence.