Certified Applied Artificial Intelligence 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 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
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Pre-requisite
No pre-requisite. It is preferred that participants completed Data Analytics Essentials or Python Programming Essentials
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
Key Features
- 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
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.
Examination
Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Digital Transformation based on the syllabus covered
Participants are expected to score a minimum of 70% to pass the examination
Why Certified Applied Artificial Intelligence Professional (CAAI)?
- 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,
Course Outline
Certified Applied Artificial Intelligence Professional (CAAI) involves immersive hands-on experiences, thorough utilization of real-world case studies, practical exercises, and collaborative group discussions.
Be Recognized as a CASUGOL Certified Professional
Add Value to your already prolific portfolio a Certificate of Competency from CASUGOL.
Awarded upon completing and meeting all requirements of the course.
All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.
Stories from Our Learners
Find out what it's like to take a CASUGOL Course from current and past learners.
Advance Your Business | Career to the Next Level
Face-to-Face, Online Zoom, and Self-paced Learning options available. Click on Clear to select your preferred batch.
For corporate booking or course customization, Email Us or Speak to our Education Officer
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Need More Information?
Have a question about CASUGOL Courses, Schedule Information, or enrolment?
Chat with an Education Officer or Email: hello@casugol.com
Course Features
- Lectures 48
- Quiz 0
- Duration 16 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Self
- 7 Sections
- 48 Lessons
- 16 Hours
- MODULE 1 - Introduction to Applied Artificial Intelligence6
- 1.0What is Applied Artificial Intelligence?
- 1.1Understanding the Concepts of Artificial Intelligence
- 1.2Real World Applications of Applied Artificial Intelligence
- 1.3Relationship Between Data Science and Artificial Intelligence
- 1.4Introduction to Machine Learning, Deep Learning, and Neural Networks
- 1.5Data Management and Governance for Artificial Intelligence
- MODULE 2 - Deep Dive into Python Programming5
- 2.0Introduction to Python Editors and IDE
- 2.1Basic Programming Rules in Python
- 2.2Understanding Variables in Python – Integers, Float, and Strings
- 2.3Conditional Operators and Control Loops in Python – If, Else if, For, While
- 2.4Packages / Libraries in Python for Artificial Intelligence – NumPy, Pandas, SciPy, Scikit-Learn, MatPlotLib
- MODULE 3 - Data Pre-processing and Cleaning for Applied Artificial Intelligence5
- MODULE 4 - Machine Learning Regression, Classification, and Clustering Techniques8
- 4.0Introduction to Regression Modelling
- 4.1What is a Linear Regression Model, Multiple Linear Regression Model and Logistic Regression Model
- 4.2Model Validation, Prediction and Refining of Regression Models
- 4.3Key Components of Classification Models in Machine Learning
- 4.4Difference Between Supervised vs. Unsupervised Classification
- 4.5Classification Techniques – Decision Tree Classification, Random Forest Classification, and Naïve Bayes Classification
- 4.6What is Clustering Analysis
- 4.7Introduction to K-Means Clustering and Hierarchical Clustering
- MODULE 5 - Deep Learning Techniques in Applied Intelligence6
- 5.0Introduction to Deep Learning
- 5.1Common Deep Learning Algorithms – MLP, BM, RBM, DBN, Autoencoders
- 5.2Neural Networks in Deep Learning
- 5.3The main characteristics of Neural Networks
- 5.4Introduction to Python TensorFlow and KERAS for Deep Learning
- 5.5Developing a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)
- MODULE 6 - Natural Language Processing (NLP) in Applied Artificial Intelligence8
- 6.0What is Natural Language Processing (NLP)?
- 6.1Text Pre-processing for Natural Language Processing (NLP)
- 6.2Understanding Recurrent Neural Networks
- 6.3Using Recurrent Dropout to Fight Overfitting
- 6.4Stacking Recurrent Layers
- 6.5Using Bidirectional RNNs
- 6.6Understanding 1D Convolution for Sequence Data
- 6.7Combing CNNs and RNNs to Process Long Sequences
- MODULE 7 - Computer Vision (CV) in Applied Artificial Intelligence10
- 7.0Introduction to Computer Vision in Applied Artificial Intelligence
- 7.1What is Convnets in Computer Vision
- 7.2Understanding Convolution Operation and Max Pooling Operation
- 7.3Training a Convnet on a Small Dataset
- 7.4Understanding the Relevance of Deep Learning for Small-Data Problems
- 7.5Downloading the Data and Building the Network
- 7.6Data Pre-processing and Data Augmentation
- 7.7Visualizing Intermediate Activations
- 7.8Visualizing Convnet Filters
- 7.9Visualizing Convnet Filters
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