3 to 5 Jul 2023 - Certified Applied AI Professional, Bangkok, Thailand

Certified Enterprise AI Professional (CEAI)

Explore Python for Enterprise AI, using its libraries to build Machine Learning, Deep Learning, NLP, and Computer Vision solutions. Engage in practical, real-world exercises to transform AI concepts into strategies and applications that drive enterprise-scale innovation.

Course Benefits

Demonstrates a High Level of Expertise and Proficiency in Applied Artificial Intelligence (AI)
Global Recognition
Networking Opportunities
Career Advancement
Job Opportunities in the Digital Economy
Increase Earning Potential
Develop a strong foundation in AI technologies and critical thinking skills,

Pre-requisite

No pre-requisite. Suitable for everyone with and without prior technology experience.

Who Should Attend

Business Leaders, System Analyst, Technologist, System Engineer, IT Professionals, Consultants, Statisticians, Software Developers, Business Process Outsourcing (BPO) Professionals, Anyone interested in acquiring knowledge and skills in Artificial Intelligence (AI) or Machine Learning

Course Objective

Acquire advanced knowledge and skills on developing Artificial Intelligence (AI) models in Artificial Intelligence (AI) technologies and its application beyond just operating GEN AI tools.

Learn how to use Python Programming x GEN AI for Machine Learning, Deep Learning, Natural Language Processing (NLP) and basic computer vision.

Course Overview

The adoption of Artificial Intelligence (AI) is expected to increase in the digital economy. Businesses are prioritizing the industrialization and professionalization of Artificial Intelligence (AI) initiatives to enhance operational efficiency and predictability. From the tackling of financial fraud and solving manufacturing automation problems to e-commerce recommendation systems. Applied Artificial Intelligence (AI) is expected to become mainstream, disrupting almost all industries and verticals.

Duration: 16 Hours

Certification: Participants will receive a Certificate of Competency upon completing the course and passing the examination

Course Outline

Module 1 – Introduction to Applied Artificial Intelligence

What is Applied Artificial Intelligence?
Understanding the Concepts of Artificial Intelligence
Real World Applications of Applied Artificial Intelligence
Relationship Between Data Science and Artificial Intelligence
Introduction to Machine Learning, Deep Learning, and Neural Networks
Data Management and Governance for Artificial Intelligence

Module 2 – Deep Dive into Python Programming

Introduction to Python Editors and IDE
Basic Programming Rules in Python
Understanding Variables in Python – Integers, Float, and Strings
Conditional Operators and Control Loops in Python – If, Else if, For, While
Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib

Module 3 – Data Pre-processing and Cleaning for Applied Artificial Intelligence

Understanding the Different Types of Data
Reading and Writing Data from Various Sources
Data Preparation for Pre-processing and Cleaning
Techniques for Data Manipulation using Python Tools
Data Formatting, Normalization, and Data Encoding

Module 4 – Machine Learning Regression, Classification, and Clustering Techniques

Introduction to Regression Modelling
What is a Linear Regression Model, Multiple Linear Regression Model and Logistic Regression Model
Model Validation, Prediction and Refining of Regression Models
Key Components of Classification Models in Machine Learning
Difference Between Supervised vs. Unsupervised Classification
Classification Techniques – Decision Tree Classification, Random Forest Classification, and Naïve Bayes Classification
What is Clustering Analysis
Introduction to K-Means Clustering and Hierarchical Clustering

What is Applied Artificial Intelligence?
Understanding the Concepts of Artificial Intelligence
Real World Applications of Applied Artificial Intelligence
Relationship Between Data Science and Artificial Intelligence
Introduction to Machine Learning, Deep Learning, and Neural Networks
Data Management and Governance for Artificial Intelligence

Introduction to Python Editors and IDE
Basic Programming Rules in Python
Understanding Variables in Python – Integers, Float, and Strings
Conditional Operators and Control Loops in Python – If, Else if, For, While
Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib

Understanding the Different Types of Data
Reading and Writing Data from Various Sources
Data Preparation for Pre-processing and Cleaning
Techniques for Data Manipulation using Python Tools
Data Formatting, Normalization, and Data Encoding

Introduction to Regression Modelling
What is a Linear Regression Model, Multiple Linear Regression Model and Logistic Regression Model
Model Validation, Prediction and Refining of Regression Models
Key Components of Classification Models in Machine Learning
Difference Between Supervised vs. Unsupervised Classification
Classification Techniques – Decision Tree Classification, Random Forest Classification, and Naïve Bayes Classification
What is Clustering Analysis
Introduction to K-Means Clustering and Hierarchical Clustering

Module 5 – Deep Learning Techniques in Applied Intelligence

Introduction to Deep Learning
Common Deep Learning Algorithms – MLP, BM, RBM, DBN, Autoencoders
Neural Networks in Deep Learning
The main characteristics of Neural Networks
Introduction to Python TensorFlow and KERAS for Deep Learning
Developing a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)

Module 6 – Natural Language Processing (NLP) in Applied Artificial Intelligence

What is Natural Language Processing (NLP)?
Text Pre-processing for Natural Language Processing (NLP)
Understanding Recurrent Neural Networks
Using Recurrent Dropout to Fight Overfitting
Stacking Recurrent Layers
Using Bidirectional RNNs
Understanding 1D Convolution for Sequence Data
Combing CNNs and RNNs to Process Long Sequences

Module 7 – Computer Vision (CV) in Applied Artificial Intelligence

Introduction to Computer Vision in Applied Artificial Intelligence
What is Convnets in Computer Vision
Understanding Convolution Operation and Max Pooling Operation
Training a Convnet on a Small Dataset
Understanding the Relevance of Deep Learning for Small-Data Problems
Downloading the Data and Building the Network
Data Pre-processing and Data Augmentation
Visualizing Intermediate Activations
Visualizing Convnet Filters

Introduction to Deep Learning
Common Deep Learning Algorithms – MLP, BM, RBM, DBN, Autoencoders
Neural Networks in Deep Learning
The main characteristics of Neural Networks
Introduction to Python TensorFlow and KERAS for Deep Learning
Developing a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)

What is Natural Language Processing (NLP)?
Text Pre-processing for Natural Language Processing (NLP)
Understanding Recurrent Neural Networks
Using Recurrent Dropout to Fight Overfitting
Stacking Recurrent Layers
Using Bidirectional RNNs
Understanding 1D Convolution for Sequence Data
Combing CNNs and RNNs to Process Long Sequences

Introduction to Computer Vision in Applied Artificial Intelligence
What is Convnets in Computer Vision
Understanding Convolution Operation and Max Pooling Operation
Training a Convnet on a Small Dataset
Understanding the Relevance of Deep Learning for Small-Data Problems
Downloading the Data and Building the Network
Data Pre-processing and Data Augmentation
Visualizing Intermediate Activations
Visualizing Convnet Filters

This course involves extensive practical/hands-on exercises, rigorous usage of real-time case studies, role-playing and group discussion

Examination

Upon completion of the course, participants are required to attempt an examination. This exam tests a candidate’s knowledge and skills related to Applied Artificial Intelligence based on the syllabus covered.

Participants are expected to score a minimum of 70% to pass the examination