Certified Artificial Intelligence Practitioner (Online)
Machine Learning & Deep Learning Hands-on, Five-day Online Instructor-led Course
Instructor-led Online course
Timing: 17:00 - 24.00 (Dubai time)
The course includes all materials, Lab Access and Exam Voucher
Questions can be addressed by email: [email protected]
Call or WhatsUp: +971 544 054 188
- Specify a general approach to solve a given business problem that uses applied AI and ML.
- Collect and refine a dataset to prepare it for training and testing.
- Train and tune a machine learning model.
- Finalize a machine learning model and present the results to the appropriate audience.
- Build linear regression models.
- Build classification models.
- Build clustering models.
- Build decision trees and random forests.
- Build support-vector machines (SVMs).
- Build artificial neural networks (ANNs).
- Promote data privacy and ethical practices within AI and ML projects.
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic C: Formulate a Machine Learning Problem
- Topic D: Select Appropriate Tools
- Topic A: Collect the Dataset
- Topic B: Analyze the Dataset to Gain Insights
- Topic C: Use Visualizations to Analyze Data
- Topic D: Prepare Data
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
- Topic A: Build a Regression Model Using Linear Algebra
- Topic B: Build a Regularized Regression Model Using Linear Algebra
- Topic C: Build an Iterative Linear Regression Model
- Topic A: Train Binary Classification Models
- Topic B: Train Multi-Class Classification Models
- Topic C: Evaluate Classification Models
- Topic D: Tune Classification Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
- Topic A: Protect Data Privacy
- Topic B: Promote Ethical Practices
- Topic C: Establish Data Privacy and Ethics Policies
Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100)
Prof. El-Masri received his Electronic Engineering Degree (1993), his Software Engineering Master’s Degree (1994) and PhD (1997) from Grenoble National Polytechnic Institute (France).
He worked at Hokkaido University (Japan) from 1998 until 2001, leading the development of an advanced software system project for telecommunications companies NTT and DoCoMo, and he was Assistant/Associate Professor at University of Western Sydney (Australia) from 2001 until 2006.
Prof. El-Masri worked in the IT industry as a Senior Project / Program Manager in leading IT consulting companies in Sydney, Australia from 2006 until 2009. Prof. El-Masri was a Professor and Senior eHealth Industry Consultant from 2009 until 2014 at King Saud University (Saudi Arabia). He has more than 100 published research papers on advanced digital technologies in international journals, books and conferences.
Prof. El-Masri worked for General Electric (GE) from 2014 as a Senior Regional Director for industrial Internet and digital projects in the MENA region until 2017. Prof. El-Masri is Certified Artificial Intelligence Practitioner, Artificial Intelligence for business, Certified in Digital Business Transformation Management, PRINCE2, Certified Blockchain Expert, and he now works as a Senior Consultant, expert and professional Trainer in Digital Transformation, Artificial Intelligence, Blockchain, Big Data Analytics, Data Science, Machine Learning, cloud platforms, and Internet of Things (IoT).
Prof. El-Masri is a public speaker, the founder and the CEO of Digitalization providing consulting and training services to the large companies and organizations in the region on Digital Transformation and emerging Digital Technologies.