Certified Applied Artificial Intelligence (AI) Professional - Onsite @ Bangkok, Thailand

International Acclaimed Certification. 5-Star Reviews

Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.

Duration: 3 Day (Onsite) / 24 Hours (Online via Zoom)

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

Who Should Attend: IT, Data, Data Managers, Data Analytics, Statistic, software developers and anyone seeking to acquire advanced knowledge on Applied artificial Intelligence.

Course Objective

• Acquire advanced knowledge and skills on artificial intelligence and its application.
• Learn how to use Python Programming language for Machine Learning, Deep Learning, Natural Language Processing (NLP) and basic computer vision.

Pre-Requisite

No pre-requisite. Certified Applied AI Professional (CAAI) is suitable for anyone who is interested in Applied Artificial Intelligence and does not have any prior technological experience

Examination

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

Module 1 Introduction to Applied Artificial Intelligence

Applied artificial intelligence (AI) is a branch of computer science that focuses on creating practical solutions using machine learning and other AI techniques. It involves developing and implementing algorithms and models that can learn from and make predictions based on data.

Topics Covered

• 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 for Applied Artificial Intelligence

Python is used extensively in various AI applications such as natural language processing, computer vision, and predictive analytics. Its popularity among data scientists and AI developers has led to the creation of many useful libraries and tools, making Python an essential language for applied AI development.

Topics Covered

• 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
• Introduction to List (Array) and Dictionary Comprehension in Python
• Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib

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

Data pre-processing and cleaning are essential steps in the development of applied AI models. It involves preparing and organizing raw data to ensure that it is suitable for analysis and use in machine learning algorithms.

Topics Covered

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

Module 4 Machine Learning Regression, Classification and Clustering Techniques

Regression, classification, and clustering are the three main techniques used in ML. Regression is a method for predicting continuous numerical output based on input variables, while classification is used for predicting categorical output based on input variables.

Topics Covered

• 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 in Applied Artificial Intelligence

Deep learning techniques are widely used in applied AI because they can achieve state-of-the-art performance in tasks such as image and speech recognition, natural language processing, and recommendation systems. Examples of deep learning architectures include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).

Topics Covered

• 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

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is widely used in applied AI, particularly in fields such as virtual assistants, chatbots, and sentiment analysis.

Topics Covered

• What is Natural Language Processing (NLP)
• Text Pre-processing for Natural Language Processing (NLP)
• Understanding Recurrent Neural Networks
• What is a First Recurrent Baseline?
• 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

CV is used in applied AI to solve problems such as object recognition, image segmentation, and facial recognition. CV involves several techniques, including image preprocessing, feature extraction, and deep learning-based approaches such as Convolutional Neural Networks (CNNs).

Topics Covered

• 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
• Visualizing Heatmaps of Class Activation

Certified Applied AI Professional (CAAI) involves extensive practical / hands-on exercises, rigorous usage of real-time case studies, role playing and group discussion