Course Program
Instructor
Certificate
Bonuses

Course Program
Instructor
Certificate
Bonuses

Become a Certified AI Business Leader

A comprehensive course program designed for leaders and professionals aiming to integrate artificial intelligence into their business strategies effectively.

Signup for the course

27 Essential Video Trainings

Multiple Choice Quizzes

Supporting Materials

This course is suitable for

Business Executives and Managers

This course is tailored for business executives and managers who aim to harness the power of AI to drive strategic decision-making and innovation. Participants will learn to identify AI opportunities, oversee AI project implementation, and leverage AI to enhance business performance and competitiveness.

Technology Professionals and AI Enthusiasts

Designed for technology professionals and AI enthusiasts, this course bridges the gap between technical expertise and business acumen. Participants will gain insights into how AI can be applied to solve real-world business challenges, enabling them to contribute effectively to AI-driven initiatives within their organizations.

Consultants and Advisors

Ideal for consultants and advisors, this course provides the knowledge and skills needed to guide clients through the AI adoption journey. Participants will learn to assess business needs, develop AI strategies, and provide informed recommendations, positioning themselves as trusted advisors in the rapidly evolving AI landscape.

Learning process

Study the topic

The course consists of practical video lessons.

Complete quizzes

Step by step, you will master new skills.

Apply your knowledge

Use the knowledge and skills you have gained in real life.

Instructor

Mostafa Kaouri

I have more than 25 years experience in AI and Digital Transformation. I wrote two books about AI. The first one is titled “Unlocking the power of AI: A Guide for Business Leaders to Drive Transformation and Growth”. The second book is about “100 AI Use Cases in the Upstream Industry: A Comprehensive Guide for Professionals and Researchers to Overcome Industry Challenges Using AI and Python.” Also, I obtained a postgraduate in AI for Leaders from the University of Texas at Austin, and Data Science with Python specialization from the University of Michigan.

Course program

What are AI and ML?

Explore the fundamentals of AI and ML, understanding their capabilities to perform human-like tasks and learn from data.

The History of AI

Trace the evolution of AI from its inception in the 1950s to its modern-day applications, exploring key milestones and breakthroughs.

ANI vs. AGI vs. ASI

Explore the distinctions between Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

The Building Blocks of AI

Understand the core components essential for developing AI systems, including algorithms, data, computing power, human expertise, and ethical considerations.

Misconceptions about AI

Clarify common misconceptions about AI, such as confusing it with robotics, fearing job replacement, and believing it to be infallible or a one-time process.

How AI is Changing the World

Discover how AI is impacting the world around us, from automating tasks in finance and medicine to enhancing transportation and entertainment.

AI Applications in Various Industries

Explore how AI is revolutionizing industries such as healthcare, finance, retail, and transportation by enhancing efficiency, safety, and personalization.

AI vs. Humans Debate

Understand the strengths and limitations of both, emphasizing the importance of balancing technological advancements with human judgment and ethical considerations.

Challenges of AI

Explore the critical challenges AI faces. This module discusses how organizations can address these issues to ensure AI's effective and ethical deployment, maximizing its potential benefits while minimizing risks.

The Need for Designing Safe AI

Addressing the importance of designing safe AI systems, this module highlights the challenges of unpredictable AI behavior, bias, and security risks.

Guidelines for Creating Safe AI

Learn about the critical guidelines for creating safe and responsible AI, focusing on safety, transparency, and accountability. This module covers best practices from leading AI organizations like OpenAI and Google.

When to Apply AI

Determine the optimal scenarios for applying AI by considering the nature of the problem, the availability of high-quality data, and your organization's expertise and resources.

Building AI Capabilities for an Organization

Learn how to build AI capabilities in your organization by identifying improvement areas, developing a clear strategy, and acquiring the necessary technology and talent.

Building Your AI Strategy

Develop a successful AI strategy by defining your business goals, assessing your data and technology, identifying specific use cases, creating a flexible roadmap, and building a skilled AI team.

Timelines for Building an AI Strategy

This module provides a detailed timeline to ensure successful AI integration and the achievement of your AI goals.

Key Roles in AI and ML

Explore the emerging roles in AI and ML, including Data Scientist, Machine Learning Engineer, AI Engineer, Research Scientist, AI Product Manager, AI Designer, and Business Intelligence Analyst.

The Types of ML

Discover the various types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning.

Common Terminologies in ML

Familiarize yourself with key ML terminologies, including algorithms, models, features, labels, training, validation, overfitting, regularization, hyperparameters, and gradient descent.

Common Techniques in ML

Explore key ML techniques, including linear regression, logistic regression, decision trees, random forests, Naïve Bayes, k-nearest neighbors, support vector machines, neural networks, GANs, and dimensionality reduction.

Common Programming Languages in ML

Explore the most widely used programming languages in ML. This module highlights each language's strengths and weaknesses, helping you choose the best one based on your project's specific requirements and goals.

Common Data Types in ML

Learn about the various data types used in ML, including numeric, categorical, binary, ordinal, time series, text, image, and video data. This module explains each type's characteristics and examples.

Data Extraction and Collection

Discover the crucial role of data extraction and collection in ML, focusing on automated methods like web scraping and APIs to gather high-quality, structured data from various sources.

Exploratory Data Analysis (EDA)

Explore the essential step of Exploratory Data Analysis (EDA) in developing ML models, focusing on understanding data patterns, relationships, and anomalies.

Hypothesis Generation

Learn the importance of hypothesis generation in developing ML models, which involves creating and testing potential explanations for relationships between variables.

Basics of Building ML Models

Learn the essential steps in building ML models, including data preparation, algorithm selection, model training, evaluation, fine-tuning, and deployment.

Evaluation of ML Model

Explore essential techniques for evaluating ML models, including splitting data into training and testing sets, using metrics for classification and regression, cross-validation, and hyperparameter tuning.

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Feedback

What our students are saying about this course?

Hammoud

The course material was well-structured, easy to follow, and provided a comprehensive overview of artificial intelligence concepts. The hands-on exercises helped in keeping me engaged even with the big amount of information. I highly recommend this course to anyone looking to delve into the exciting field of artificial intelligence.