Certified AI and Machine Learning Expert

As AI and machine learning redefine the future of technology, the Global Tech Council proudly introduces the Certified AI and Machine Learning Expert Certification. This comprehensive programme empowers you with in-depth knowledge and practical skills to design, build, and deploy intelligent systems. From core algorithms to cutting-edge tools and frameworks, you’ll gain experience in solving real-world problems through AI and ML. Master the art of data-driven innovation and become a Certified AI and Machine Learning Expert—ready to shape the intelligent systems of tomorrow and lead in a world driven by smart, scalable, and transformative solutions.

$299

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Course duration

16 Hours

Exam

Self-paced

Access Mode

Online

Certification Validity

Lifetime

Modules Included

Module 1: Introduction to Certified AI and ML Expert

Introduction to the certification

Module 2: Introduction to AI

Introduction to Artificial Intelligence (AI)
Brief history and evolution of AI
Current state of AI and future trends

Module 3: Foundations of AI

Types of AI: Narrow AI, General AI, and Superintelligent AI
Major Approaches to AI: Symbolic, Machine Learning, and Hybrid

Module 4: AI Technologies and Algorithms

Fundamental Algorithms in AI and ML
Introduction to Neural Networks
Architecture based Neural Networks ( FNNs, RNNs/LSTM, Transformer)
Data processing based Neural Networks ( CNNs, GNNs)
Functionality based Neural Networks ( GANs, SNNs)
Natural Language Processing (NLP)
Computer Vision (CV)

Module 5: Practical Applications of AI

AI in Healthcare
AI in Business
AI in Autonomous Vehicles
AI in Entertainment
AI in Finance

Module 6: AI in Technology

AI with Blockchain
AI in Technology
AI for Edge computing

Module 7: Explainable AI (XAI)

Basics of XAI

Module 8: AI Ethics

Understanding AI Ethics
Potential Pitfalls and Controversies in AI
Strategies for Responsible AI Deployment

Module 9: Generative AI and Prompt Engineering

Generative AI
What is Prompt Engineering?
Importance of Prompt Engineering
Applications of Prompt Engineering

Module 10: Fundamentals of ML

What is Machine Learning?
Types of Machine Learning
Importance of Data in ML
Bias and Variance in ML Curse of Dimensionality
Dimensionality Reduction
Difference Between AI, ML, and Deep Learning

Module 11: Data Preprocessing

Importing data
Data Cleaning
Data Transformation
Feature Engineering Concepts
Feature Scaling and Normalization
Feature Extraction

Module 12: Supervised Learning Regression

Supervised Learning
Overfitting vs. Underfitting

Module 13: Regression

Linear Regression
Polynomial Regression
Decision Tree Regression
Random Forest Regression

Module 14: Classification

Logistic Regression
Support Vector Machine
Naive Bayes

Module 15: Unsupervised Learning

Clustering using K-means in Python
Hierarchical Clustering
Applications of Clustering in Real Life
Difference Between Supervised and Unsupervised Learning

Module 16: Reinforcement Learning

Reinforcement Learning
Concept of Rewards and Penalties in RL
Types of RL Algorithms (Value-Based, Policy-Based, Model-Based)

Module 17: Reinforcement Learning

Basics of Model Deployment
Introduction to MLOps

Module 18: Deep Learning

Introduction to Deep Learning
Introduction to Optimization
Activation Functions in Neural Networks
Transformers

Module 19: Federated Learning

Federated Learning

Module 20: Natural Language Processing

Natural Language Processing (part 1)
Natural Language Processing (part 2)

Module 21: Time Series Forecasting

Stationarity in Time Series
Time Series Decomposition

Module 22: Ethical and Advanced Concepts

Ethical Concerns in ML (Bias, Fairness, Transparency)
Interpretability of ML Models
Future Trends in ML
AutoML (Automated Machine Learning)
Gradient Boosting

Module 23: Cloud & AI Services

AI and ML in cloud

Module 24: Interactive coding and AI labs

Introduction to
Interactive coding and AI Labs
Interactive AI coding environment
Rise of AI augmented coding

Module 25: ML in Cybersecurity

Role of Machine Learning in Cybersecurity
Difference Between Traditional Security and AI-Driven Security

Module 26: ML Techniques for Cybersecurity

Anomaly Detection in Network Security
Supervised vs. Unsupervised Learning for Threat Detection
Deep Learning for Intrusion Detection Systems (IDS)
Behavior-Based Malware Detection

Module 27: Applications of ML in Cybersecurity

Phishing Detection Using ML
Spam Filtering and Email Security
Fraud Detection in Financial Transactions
ML in Identity and Access Management (IAM)

Top Job Roles

Who is a Certified AI and Machine Learning Expert?

A Certified AI and Machine Learning Expert is a highly trained professional with validated expertise in building intelligent systems using AI and ML technologies. These individuals excel at designing, training, and deploying machine learning models to solve complex real-world problems. With a deep understanding of data, algorithms, and AI frameworks, they lead the charge in developing smart, scalable, and impactful solutions. Certified AI and Machine Learning Experts play a vital role in driving innovation across industries, turning data into intelligence and shaping the future of automation and decision-making.

Who Is the Ideal Candidate for the Certified AI and Machine Learning Expert Certification?

This certification is ideal for professionals and enthusiasts who are passionate about artificial intelligence, data, and intelligent automation—whether you’re a data scientist, software developer, analyst, or aspiring AI specialist. It suits both experienced tech professionals looking to deepen their ML expertise and beginners aiming to break into the AI field. If you’re eager to master machine learning models, algorithms, and real-world AI applications, this programme is your gateway to success in one of the most dynamic and in-demand tech domains.

What Are the Responsibilities of a Certified AI and Machine Learning Expert?

Certified AI and Machine Learning Experts are responsible for designing, developing, and deploying intelligent systems that solve real-world problems using data-driven approaches. They apply advanced knowledge of algorithms, statistical modelling, and AI frameworks to build predictive models, automate decision-making, and enhance system capabilities. Working closely with cross-functional teams, they turn raw data into actionable insights and integrate AI solutions across domains like finance, healthcare, cybersecurity, and beyond. Their core mission is to ensure accuracy, scalability, and impact in AI-powered systems while driving innovation in the evolving landscape of intelligent technology.

In Which Domains Does a Certified AI and Machine Learning Expert Operate?

Artificial Intelligence & Automation
Data Science & Analytics
Healthcare & Biotechnology
Finance & Fintech
Retail, E-commerce & Personalisation

Certification Benefits

Comprehensive Knowledge of AI and Machine Learning:Expertise in core AI principles, machine learning algorithms, and advanced tools used to build, train, and deploy intelligent models across diverse applications.
Recognition of Expertise:This certification serves as a solid validation of your proficiency in AI and machine learning, enhancing your professional credibility and opening doors to advanced career opportunities.
Broadened Career Opportunities:Opens pathways to high-demand roles in artificial intelligence, machine learning, data science, AI research, and intelligent system development across various industries.
Professional Recognition:Establish yourself as a Certified AI and Machine Learning Expert, showcasing your leadership and credibility in building intelligent, data-driven solutions that shape the future of technology.

Frequently Asked Questions

Who Is a Certified AI and Machine Learning Expert?

A Certified AI and Machine Learning Expert is a recognised professional with validated expertise in designing, developing, and deploying intelligent systems using AI and machine learning techniques.

Who Is the Certified AI and Machine Learning Expert Certification For?

Ideal for data scientists, AI enthusiasts, software engineers, analysts, and professionals aiming to build a career in artificial intelligence and machine learning.

What Are the Advantages of Becoming a Certified AI and Machine Learning Expert?

Gain comprehensive knowledge of AI and machine learning, earn industry-recognised certification, unlock advanced career opportunities, and join a network of forward-thinking professionals with access to ongoing learning and innovation.

What Is the Duration of the Certification Process?

The certification includes approximately 16 hours of self-paced learning, complete with lifetime access to course materials and an online exam to validate your expertise.

Can I Retake the Certification Exam if I Don’t Pass on the First Attempt?

Yes, you can retake the exam if you don’t pass the first time. Simply refer to the certification guidelines for details on the retake process and any applicable conditions.

What Are the Requirements for Enrolling in the Certified AI and Machine Learning Expert Program?

There are no strict prerequisites for enrolment, but a foundational understanding of programming, mathematics, and basic AI concepts will help you make the most of the course.

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Global Tech Council is a platform bringing techies from all around the globe to share their knowledge, passion, expertise and vision on various in-demand technologies, thereby imparting valuable credentials to individuals seeking career growth acceleration.