Become a Professional AI Engineer: Deep Learning, MLOps & AI Agents

What you will learn:

Master advanced model optimization techniques
Build high-performing CNNs for computer vision
Develop robust RNNs and LSTMs for sequence modeling
Utilize the power of transformers and attention mechanisms
Apply transfer learning strategies for efficient model adaptation
Design and implement intelligent AI agents
Gain proficiency in TensorFlow and PyTorch
Deploy production-ready ML models using MLOps best practices

Description

Transform your AI skills from theory to production with this comprehensive AI Engineer Professional Certificate program. This isn't just another deep learning course; it's a complete journey to mastering advanced AI engineering techniques, including the latest advancements in transformer architectures and AI agent development.

You'll begin by mastering model optimization techniques, from basic hyperparameter tuning using grid and random search to advanced Bayesian optimization and regularization strategies. Learn how to build robust and efficient models with cross-validation and automated tuning pipelines. This course goes beyond theory, providing extensive hands-on experience.

Build a strong foundation in computer vision by creating Convolutional Neural Networks (CNNs) from scratch using TensorFlow and PyTorch. Master convolutional and pooling layers, and apply your skills to real-world image classification and object detection tasks.

Expand your expertise into sequence modeling with Recurrent Neural Networks (RNNs), LSTMs, and GRUs. Tackle the challenges of temporal data analysis, mastering time series, text, and speech processing. You will learn advanced techniques for handling vanishing gradients and long-term dependencies.

Explore the power of transformers, the backbone of today's cutting-edge AI. Dive deep into self-attention and multi-head attention mechanisms, building and applying transformer models such as BERT and GPT. This course goes beyond simply using pre-trained models; you will learn to build them from the ground up.

Unlock the practical power of transfer learning and fine-tuning, techniques crucial for building high-performing models efficiently. Learn how to leverage pre-trained architectures to adapt quickly to specific tasks and domains, saving time and resources.

Dive into the world of AI agents, learning to build autonomous systems capable of reactive behavior and goal-oriented actions. Explore diverse agent architectures and see how they're utilized in real-time decision-making and simulations.

Finally, master the art of MLOps, learning to deploy, monitor, and maintain AI systems in production. Gain expertise with Docker, MLflow, Kubeflow, and CI/CD pipelines to ensure your models are robust, scalable, and reproducible. This course will equip you with the essential skills for model versioning and production deployment.

Upon completion, you'll be equipped with the advanced skills needed for roles such as Machine Learning Engineer, AI Researcher, or Lead AI Architect. Enroll today and earn your AI Engineer Professional Certificate!

Curriculum

Introduction to Course and Instructor

This introductory section sets the stage for the course, outlining what you'll learn and introducing the instructor. The single lecture, 'What You’ll Learn in the AI Engineer Professional Certificate Course,' provides a comprehensive overview of the course content and structure, preparing you for the journey ahead.

Model Tuning and Optimization

This section dives into the crucial aspects of model optimization. You'll begin with an introduction to hyperparameter tuning and progress through various techniques, including Grid Search, Random Search, and Bayesian Optimization. You’ll also learn about regularization, cross-validation, and automated tuning pipelines, culminating in a hands-on project where you build and fine-tune a model for loan approval prediction, applying all the techniques learned throughout the section.

Convolutional Neural Networks (CNNs)

This section provides a comprehensive understanding of CNNs. You will cover the fundamental concepts of convolutional layers, pooling, and dropout, building CNNs from scratch using Keras and PyTorch. The section includes a project focusing on image classification using Fashion MNIST or CIFAR-10, applying the concepts learned throughout the section and allowing you to build a functional image classifier.

Recurrent Neural Networks (RNNs) and Sequence Modeling

This section delves into RNNs and sequence modeling. You will learn about RNN architecture, backpropagation through time (BPTT), LSTMs, and GRUs. You'll also explore text preprocessing, word embeddings, and sequence-to-sequence models. The culminating project involves building an RNN for tasks like text generation or sentiment analysis, allowing you to put your knowledge into practice.

Transformers and Attention Mechanisms

This section explores the power of transformers and attention mechanisms. You will learn about self-attention, multi-head attention, positional encoding, and feed-forward networks. You will work with pre-trained transformers like BERT and GPT, and then complete a final project involving text summarization or translation, showcasing your mastery of the techniques discussed.

Transfer Learning and Fine-Tuning

This section focuses on the efficient techniques of transfer learning and fine-tuning. You'll explore how to leverage pre-trained models for both computer vision and NLP tasks, learning about feature extraction and fine-tuning strategies. The section culminates in a project where you fine-tune a pre-trained model for a custom task, demonstrating your ability to adapt powerful models for specific needs.

AI Agents: A Comprehensive Overview

This section introduces the concept of AI agents. You'll explore different agent architectures (reactive, goal-based, multi-agent systems) and work hands-on with frameworks like AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT, building practical AI agents and learning to select appropriate frameworks based on specific application requirements.

Introduction and Hands-on MLOps

This section provides a practical introduction to MLOps principles and practices. You will learn about deploying, monitoring, and maintaining ML models in production using tools like Docker, MLflow, Kubeflow, and CI/CD pipelines. The section includes hands-on exercises covering setting up MLOps project structures, building end-to-end pipelines, containerizing models using Docker, and deploying them locally using Kubernetes.

Congratulations

A brief concluding section congratulating you on completing the course and wishing you success in your AI career.