AI Engineer Professional Certificate Course

Master Deep Learning, Transformers, MLOps & AI Agent Development with Real-World Projects

What you'll learn
Tune and optimize machine learning models using advanced techniques
Build and train CNNs for image classification and computer vision tasks
Develop RNNs, LSTMs, and GRUs for time series and sequence modeling
Understand and implement transformers and attention mechanisms
Apply transfer learning to fine-tune powerful pre-trained models
Design and analyze AI agents for autonomous decision-making
Use TensorFlow and PyTorch for deep learning projects
Deploy models using MLOps tools like Docker, MLflow, and CI/CD pipelines

Requirements
Completion of a beginner or associate-level AI or machine learning course (or equivalent knowledge)
Strong understanding of Python programming, including experience with functions, classes, and working with libraries like NumPy and Pandas
Solid grasp of basic machine learning concepts, including regression, classification, model evaluation, and overfitting
Familiarity with deep learning fundamentals, including neural networks and basic model architecture
Prior exposure to tools like Jupyter Notebook, TensorFlow, or PyTorch
Working knowledge of mathematics for AI, including linear algebra, probability, and calculus
A computer (Windows, macOS, or Linux) with reliable internet and the ability to install development tools
Willingness to explore complex, production-grade systems and invest time in hands-on coding, model experimentation, and deployment workflows

Description
Step into the world of advanced AI engineering with the AI Engineer Professional Certificate Course — your complete guide to mastering deep learning, model optimization, transformer architectures, AI agents, and MLOps. This expert-level program is designed for learners who are ready to level up from theory to production, building cutting-edge AI systems using real-world tools and frameworks.You’ll start with Model Tuning and Optimization, where you’ll learn how to fine-tune hyperparameters using Grid Search, Random Search, and Bayesian Optimization. Discover the impact of regularization, cross-validation, and automated tuning pipelines—crucial for increasing the accuracy and efficiency of your ML models.Next, dive deep into Convolutional Neural Networks (CNNs), the building blocks of computer vision. You’ll understand how to build CNNs from scratch, learn about convolutional layers, pooling, and dropout, and apply them to image classification, object detection, and more using TensorFlow and PyTorch.From images to sequences—Recurrent Neural Networks (RNNs) and Sequence Modeling covers the foundational principles of temporal data analysis. Learn how to model time series, text, and speech using RNNs, LSTMs, and GRUs, including how to tackle vanishing gradients and long-term dependencies.Then, prepare to explore the crown jewel of modern AI—Transformers and Attention Mechanisms. Learn how self-attention, multi-head attention, and positional encoding power models like BERT, GPT, and T5. You’ll build transformer models from scratch and apply pre-trained architectures to solve real-world problems.You’ll also master Transfer Learning and Fine-Tuning, one of the most practical skills for today’s AI engineers. Learn how to use pre-trained models and adapt them for specific tasks using feature extraction and fine-tuning strategies, saving both compute time and data.The course also includes an in-depth look at AI Agents: A Comprehensive Overview. You’ll explore the architecture of autonomous agents, including reactive agents, goal-based agents, and multi-agent systems. See how AI agents are used in real-time decision-making, game AI, personal assistants, and agent-based simulations.Finally, bring it all together in Introduction and Hands-on MLOps. Discover how to deploy, monitor, and maintain models in production using tools like Docker, MLflow, Kubeflow, and CI/CD pipelines. Learn about model versioning, reproducibility, and scalability—the skills every modern AI engineer must master.By the end of this course, you will:Tune and optimize deep learning models for productionBuild CNNs, RNNs, and Transformer-based architecturesUse transfer learning to adapt powerful models to new domainsUnderstand and design AI agents for real-world environmentsApply MLOps best practices for scalable AI deploymentWhether you're aiming to become a Machine Learning Engineer, AI Researcher, or Lead AI Architect, this is the ultimate course to make your transition from skilled practitioner to AI professional.Join today and earn your AI Engineer Professional Certificate — the gold standard in advanced AI training.

Who this course is for:
AI Engineers and Machine Learning Practitioners looking to deepen their expertise in model tuning, deep learning, and deployment, Data Scientists aiming to specialize in deep learning architectures and real-time AI systems, Software Engineers seeking to integrate AI capabilities into full-stack applications using TensorFlow and PyTorch, Graduate students or academic researchers transitioning into industry-level AI roles, Tech professionals who want to master Transformers, MLOps, and AI Agent frameworks to solve complex business problems, Anyone who has already completed an introductory AI or ML course and wants to confidently build, fine-tune, and deploy cutting-edge AI models