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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.
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$299

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  • EMI Option Available
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Course duration

16 Hours

Exam

Self-paced

Access Mode

Online

Certification Validity

Lifetime

Modules Included

  • Introduction to the certification
  • Introduction to Artificial Intelligence (AI)
  • Brief history and evolution of AI
  • Current state of AI and future trends
  • Types of AI: Narrow AI, General AI, and Superintelligent AI
  • Major Approaches to AI: Symbolic, Machine Learning, and Hybrid
  • 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)
  • AI in Healthcare
  • AI in Business
  • AI in Autonomous Vehicles
  • AI in Entertainment
  • AI in Finance
  • AI with Blockchain
  • AI in Technology
  • AI for Edge computing
  • Basics of XAI
  • Understanding AI Ethics
  • Potential Pitfalls and Controversies in AI
  • Strategies for Responsible AI Deployment
  • Generative AI
  • What is Prompt Engineering?
  • Importance of Prompt Engineering
  • Applications of Prompt Engineering
  • 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
  • Importing data
  • Data Cleaning
  • Data Transformation
  • Feature Engineering Concepts
  • Feature Scaling and Normalization
  • Feature Extraction
  • Supervised Learning
  • Overfitting vs. Underfitting
  • Linear Regression
  • Polynomial Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Logistic Regression
  • Support Vector Machine
  • Naive Bayes
  • Clustering using K-means in Python
  • Hierarchical Clustering
  • Applications of Clustering in Real Life
  • Difference Between Supervised and Unsupervised Learning
  • Reinforcement Learning
  • Concept of Rewards and Penalties in RL
  • Types of RL Algorithms (Value-Based, Policy-Based, Model-Based)
  • Basics of Model Deployment
  • Introduction to MLOps
  • Introduction to Deep Learning
  • Introduction to Optimization
  • Activation Functions in Neural Networks
  • Transformers
  • Federated Learning
  • Natural Language Processing (part 1)
  • Natural Language Processing (part 2)
  • Stationarity in Time Series
  • Time Series Decomposition
  • Ethical Concerns in ML (Bias, Fairness, Transparency)
  • Interpretability of ML Models
  • Future Trends in ML
  • AutoML (Automated Machine Learning)
  • Gradient Boosting
  • AI and ML in cloud
  • Introduction to
  • Interactive coding and AI Labs
    Interactive AI coding environment
  • Rise of AI augmented coding
  • Role of Machine Learning in Cybersecurity
  • Difference Between Traditional Security and AI-Driven Security
  • Anomaly Detection in Network Security
  • Supervised vs. Unsupervised Learning for Threat Detection
  • Deep Learning for Intrusion Detection Systems (IDS)
  • Behavior-Based Malware Detection
  • Phishing Detection Using ML
  • Spam Filtering and Email Security
  • Fraud Detection in Financial Transactions
  • ML in Identity and Access Management (IAM)

Top Job Roles

  • Machine Learning Engineer
  • AI Research Scientist
  • Data Scientist
  • Natural Language Processing (NLP) Engineer

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.

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.
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.
  • 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

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.

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

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.

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.

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.

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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