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AI Engineer Professional Certificate Course

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

Rating: 4.5175 ratings22,268 students15.5 total hours61 lectures
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Course facts

  • Last updated 02/2026
  • English English [Auto]
  • Instructor: School of AI
  • practical AI capability and workflow improvement

What you'll learn

Practical outcomes

  • 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

Curriculum

9 sections • 61 lectures • 15h 24m total length

Introduction to Course and Instructor2 lectures • 7min
  1. Certificate of Completion00:29
  2. What You’ll Learn in the AI Engineer Professional Certificate Course06:24
Model Tuning and Optimization7 lectures • 2hr 5min
  1. Day 1: Introduction to Hyperparameter Tuning13:46
  2. Day 2: Grid Search and Random Search16:09
  3. Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization26:57
  4. Day 4: Regularization Techniques for Model Optimization13:17
  5. Day 5: Cross-Validation and Model Evaluation Techniques13:00
  6. Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV19:28
  7. Day 7: Optimization Project – Building and Tuning a Final Model22:45
  8. Tuning and Validating the Final Model for Loan Approval Prediction
Convolutional Neural Networks (CNNs)7 lectures • 2hr 40min
  1. Day 1: Introduction to Convolutional Neural Networks26:16
  2. Day 2: Convolutional Layers and Filters23:48
  3. Day 3: Pooling Layers and Dimensionality Reduction23:58
  4. Day 4: Building CNN Architectures with Keras and TensorFlow17:46
  5. Day 5: Building CNN Architectures with PyTorch22:26
  6. Day 6: Regularization and Data Augmentation for CNNs18:39
  7. Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-1027:34
  8. Presenting a CNN Image Classifier for Fashion Product Categorization
Recurrent Neural Networks (RNNs) and Sequence Modeling7 lectures • 2hr 45min
  1. Day 1: Introduction to Sequence Modeling and RNNs33:32
  2. Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)24:31
  3. Day 3: Long Short-Term Memory (LSTM) Networks15:03
  4. Day 4: Gated Recurrent Units (GRUs)07:07
  5. Day 5: Text Preprocessing and Word Embeddings for RNNs24:02
  6. Day 6: Sequence-to-Sequence Models and Applications43:09
  7. Day 7: RNN Project – Text Generation or Sentiment Analysis17:55
  8. Designing an RNN for Sentiment Analysis of Customer Reviews
Transformers and Attention Mechanisms7 lectures • 2hr 14min
  1. Day 1: Introduction to Attention Mechanisms15:17
  2. Day 2: Introduction to Transformers Architecture18:19
  3. Day 3: Self-Attention and Multi-Head Attention in Transformers21:00
  4. Day 4: Positional Encoding and Feed-Forward Networks20:21
  5. Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT19:37
  6. Day 6: Advanced Transformers – BERT Variants and GPT-320:38
  7. Day 7: Transformer Project – Text Summarization or Translation18:33
  8. Deploying a Transformer Model for Text Summarization in LegalTech
Transfer Learning and Fine-Tuning7 lectures • 2hr 19min
  1. Day 1: Introduction to Transfer Learning14:52
  2. Day 2: Transfer Learning in Computer Vision26:26
  3. Day 3: Fine-Tuning Techniques in Computer Vision21:46
  4. Day 4: Transfer Learning in NLP17:00
  5. Day 5: Fine-Tuning Techniques in NLP26:04
  6. Day 6: Domain Adaptation and Transfer Learning Challenges14:52
  7. Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task18:22
  8. Fine-Tuning a Pretrained Model for Industry-Specific Email Classification
AI Agents: A Comprehensive Overview6 lectures • 1hr 24min
  1. 1. Hands-on AutoGen | IBM Bee | LangGraph | CrewAI | AutoGPT08:46
  2. 2. Hands-on AutoGen13:04
  3. 3. Hands-on IBM Bee Framework18:23
  4. 4. Hands-on LangGraph20:05
  5. 5. Hands-on CrewAI15:36
  6. 6. Hands-on AutoGPT07:52
  7. Selecting the Right Multi-Agent Framework for an AI Research Assistant
Introduction and Hands-on MLOps17 lectures • 1hr 48min
  1. 1. Introduction to MLOps Sessions00:37
  2. 2. Overview of MLOps and its Importance01:29
  3. 3. Evolution of Machine Learning Operations01:06
  4. 4. Key Concepts in MLOps: Versioning, Automation, and Monitoring01:22
  5. 5. MLOps vs. DevOps: Similarities and Differences01:12
  6. 6. Hands-on: Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline20:37
  7. 7. Introduction to Data Science to Production Pipeline Section00:24
  8. 8. Overview of the ML Workflow: Data Preparation to Deployment03:11
  9. 9. Experimentation vs. Production01:51
  10. 10. Challenges in Deploying ML Models00:58
  11. 11. Hands-on: Build an end-to-end pipeline for an ML model21:06
  12. 12. Introduction to Infrastructure for MLOps Section00:36
  13. 13. Introduction to Cloud Platforms (AWS, GCP, Azure)04:13
  14. 14. Containerization with Docker01:21
  15. 15. Kubernetes for Orchestrating ML Workloads01:20
  16. 16. Setting up Local MLOps Environments01:25
  17. 17. Hands-on: Containerize simple ML model & deploy it locally using Kubernetes45:41
  18. Reviewing Your First End-to-End MLOps Pipeline for Deployment
Final Quiz & Congratulations1 lecture • 1min
  1. Final Quiz 100 questions
  2. Congratulations and Best of Luck00:47

Who it 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

Course description

Overview

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 production Build CNNs, RNNs, and Transformer-based architectures Use transfer learning to adapt powerful models to new domains Understand and design AI agents for real-world environments Apply MLOps best practices for scalable AI deployment Whether 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.

Instructor

School of AI

School of AI AI Academy We are your one-stop shop for all AI training needs. Since 2010, our expert instructors have been delivering top-tier education across cutting-edge technologies. Whether you’re a beginner or looking to advance your skills, our comprehensive training programs are designed to equip you with practical knowledge and real-world experience. From foundational AI concepts to advanced machine learning and deep learning techniques, we cover it all. Join us to gain hands-on expertise, guided by industry veterans committed to your success. Unlock your potential and stay ahead in the evolving world of artificial intelligence with training you can trust and results you can see.