Certified Artificial Intelligence Practitioner (In-Classroom)

Machine Learning & Deep Learning Hands-on, Five-day In-Classroom Course

Certified Artificial Intelligence (AI) Practitioner (CAIP) (Exam IP-110)

Audience: Technical Professionals and Technical leaders

In-Classroom - Media One Hotel - Dubai

Course Length: 5 days (31 Jan - 04 Feb 2022)

Timing: 08:00 - 16:00 (Dubai time)

The training includes:

  • In-classroom international expert
  • Materials (Textbook + Slides)
  • Lab Access
  • Exam Voucher (Two attempts)
  • Buffet Lunch
  • Lunch break and special break for Friday
  • Two breaks with refreshment

Questions can be addressed by email to: [email protected]

Call or WhatsUp: +971 544 054 188


Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands on activities for each topic area. For a detailed outline including activities, hardware requirements and datasets please contact [email protected]

Course Objectives:
In this course, you will implement AI techniques in order to solve business problems.
You will:
  • 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.

Target Student:
The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.
So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.
A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. You can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.
You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following Logical Operations or comparable course:
• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced

Course Content
Lesson 1: Solving Business Problems Using AI and ML
  • Topic A: Identify AI and ML Solutions for Business Problems
  • Topic C: Formulate a Machine Learning Problem
  • Topic D: Select Appropriate Tools
Lesson 2: Collecting and Refining the Dataset
  • Topic A: Collect the Dataset
  • Topic B: Analyze the Dataset to Gain Insights
  • Topic C: Use Visualizations to Analyze Data
  • Topic D: Prepare Data
Lesson 3: Setting Up and Training a Model
  • Topic A: Set Up a Machine Learning Model
  • Topic B: Train the Model
Lesson 4: Finalizing a Model
  • Topic A: Translate Results into Business Actions
  • Topic B: Incorporate a Model into a Long-Term Business Solution
Lesson 5: Building Linear Regression Models
  • 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
Lesson 6: Building Classification Models
  • Topic A: Train Binary Classification Models
  • Topic B: Train Multi-Class Classification Models
  • Topic C: Evaluate Classification Models
  • Topic D: Tune Classification Models
Lesson 7: Building Clustering Models
  • Topic A: Build k-Means Clustering Models
  • Topic B: Build Hierarchical Clustering Models
Lesson 8: Building Advanced Models
  • Topic A: Build Decision Tree Models
  • Topic B: Build Random Forest Models
Lesson 9: Building Support-Vector Machines
  • Topic A: Build SVM Models for Classification
  • Topic B: Build SVM Models for Regression
Lesson 10: Building Artificial Neural Networks
  • Topic A: Build Multi-Layer Perceptrons (MLP)
  • Topic B: Build Convolutional Neural Networks (CNN)
Lesson 11: Promoting Data Privacy and Ethical Practices
  • 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)

Your Instructor

Prof. Samir El-Masri
Prof. Samir El-Masri

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.

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