Master Deep Learning, Transformers, MLOps & AI Agent Development with Real-World Projects
Note: Course Free for limited time and limited users(500 limit) so Enroll ASAP. If not free it means you are late. Scroll Down and click Enroll Now button to get enrolled
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Apply Coupon Code: DEC_FREE_AA03
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
Explore related topics
This course includes:
- Role Play
- 15.5 hours on-demand video
- 46 downloadable resources
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
Course content
9 sections • 60 lectures • 15h 23m total length
- What You’ll Learn in the AI Engineer Professional Certificate Course
- Day 1: Introduction to Hyperparameter Tuning
- Day 2: Grid Search and Random Search
- Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization
- Day 4: Regularization Techniques for Model Optimization
- Day 5: Cross-Validation and Model Evaluation Techniques
- Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
- Day 7: Optimization Project – Building and Tuning a Final Model
- Tuning and Validating the Final Model for Loan Approval Prediction
- Day 1: Introduction to Convolutional Neural Networks
- Day 2: Convolutional Layers and Filters
- Day 3: Pooling Layers and Dimensionality Reduction
- Day 4: Building CNN Architectures with Keras and TensorFlow
- Day 5: Building CNN Architectures with PyTorch
- Day 6: Regularization and Data Augmentation for CNNs
- Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10
- Presenting a CNN Image Classifier for Fashion Product Categorization
- Day 1: Introduction to Sequence Modeling and RNNs
- Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)
- Day 3: Long Short-Term Memory (LSTM) Networks
- Day 4: Gated Recurrent Units (GRUs)
- Day 5: Text Preprocessing and Word Embeddings for RNNs
- Day 6: Sequence-to-Sequence Models and Applications
- Day 7: RNN Project – Text Generation or Sentiment Analysis
- Designing an RNN for Sentiment Analysis of Customer Reviews
- Day 1: Introduction to Attention Mechanisms
- Day 2: Introduction to Transformers Architecture
- Day 3: Self-Attention and Multi-Head Attention in Transformers
- Day 4: Positional Encoding and Feed-Forward Networks
- Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT
- Day 6: Advanced Transformers – BERT Variants and GPT-3
- Day 7: Transformer Project – Text Summarization or Translation
- Deploying a Transformer Model for Text Summarization in LegalTech
- Day 1: Introduction to Transfer Learning
- Day 2: Transfer Learning in Computer Vision
- Day 3: Fine-Tuning Techniques in Computer Vision
- Day 4: Transfer Learning in NLP
- Day 5: Fine-Tuning Techniques in NLP
- Day 6: Domain Adaptation and Transfer Learning Challenges
- Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task
- Fine-Tuning a Pretrained Model for Industry-Specific Email Classification
- 1. Hands-on AutoGen | IBM Bee | LangGraph | CrewAI | AutoGPT
- 2. Hands-on AutoGen
- 3. Hands-on IBM Bee Framework
- 4. Hands-on LangGraph
- 5. Hands-on CrewAI
- 6. Hands-on AutoGPT
- Selecting the Right Multi-Agent Framework for an AI Research Assistant
- 1. Introduction to MLOps Sessions
- 2. Overview of MLOps and its Importance
- 3. Evolution of Machine Learning Operations
- 4. Key Concepts in MLOps: Versioning, Automation, and Monitoring
- 5. MLOps vs. DevOps: Similarities and Differences
- 6. Hands-on: Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline
- 7. Introduction to Data Science to Production Pipeline Section
- 8. Overview of the ML Workflow: Data Preparation to Deployment
- 9. Experimentation vs. Production
- 10. Challenges in Deploying ML Models
- 11. Hands-on: Build an end-to-end pipeline for an ML model
- 12. Introduction to Infrastructure for MLOps Section
- 13. Introduction to Cloud Platforms (AWS, GCP, Azure)
- 14. Containerization with Docker
- 15. Kubernetes for Orchestrating ML Workloads
- 16. Setting up Local MLOps Environments
- 17. Hands-on: Containerize simple ML model & deploy it locally using Kubernetes
- Reviewing Your First End-to-End MLOps Pipeline for Deployment
- Final Quiz
- Congratulations and Best of Luck
- 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 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.
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
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
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