Certified Applied AI Professional (CAAI)
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, solving manufacturing automation problems to e-commerce recommendation systems. Applied Artificial Intelligence (AI) is expected to become mainstream, disrupting almost all industries and verticals.
Certified Enterprise AI Professional (CEAI)
Price range: $680.00 through $780.00
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
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
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
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What You Can Expect
 Technology-Based Training |
 Open-Source Tools |
 80% Hands-On Exercises / 20% Theory |
 Designed by Industry Leaders and Subject-Matter Experts |
 Progressive Learning |
 Highly interactive, supportive and encouraging |
 Be recognized as a CASUGOL Certified Professional |
What Learners Say
Artificial Intelligence Shorts
Quick lessons on a wide range of topics related to Artificial Intelligence (AI). Learn pragmatic tips on Artificial Intelligence (AI) deployment strategies, and acquire technical insights that can easily be applied in different scenarios.
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