AI Engineer Professional Certificate Course

Explore the difference between parameters and hyperparameters, and learn how tuning hyperparameters like max depth, learning rate, and n estimators improves model performance, with hands-on iris dataset using random forest.

Compare grid search and random search for hyperparameter tuning, exploring exhaustive and random sampling methods. Apply these strategies to the iris dataset to optimize model performance.

Master cross-validation and model evaluation using k-fold, stratified k-fold, and leave-one-out methods for robust performance, and apply to imbalanced data like credit card fraud detection with hyperparameter tuning.

Explore automated hyperparameter tuning using grid search and randomized search, applying cross-validation to gradient boosting and support vector machines on the iris dataset.

Build and tune a final churn model using end-to-end optimization, including preprocessing, feature engineering, cross-validation, and hyperparameter tuning with randomized search; evaluate with accuracy and ROC AUC.

Explore the basics of convolutional neural networks, including convolutional layers, pooling, and fully connected layers, visualize CIFAR-10 images, and set up CNNs in TensorFlow and PyTorch.

Explore convolutional operations, filters, and feature maps to extract edges and patterns in images. Learn how kernel size, stride, and padding shape output, and apply hands-on TensorFlow and PyTorch experiments.

Explore pooling layers and dimensionality reduction in CNNs, comparing max pooling and average pooling, then implement these layers in TensorFlow and PyTorch to downsample feature maps and extract hierarchical features.

Build, train, and evaluate a CNN with Keras and TensorFlow, using conv and pooling layers, dense layers, dropout, and model.fit, then assess performance on CIFAR-10 or MNIST.

Build CNN architectures in PyTorch using the NN module, train and evaluate on CIFAR-10, and tune kernel sizes and learning rate to optimize model design and accuracy.

Explore how regularization and data augmentation curb overfitting in CNNs, using dropout, batch normalization, and image transformations like rotation, flipping, scaling, cropping, and brightness adjustment.

Apply CNN architectures to large datasets like CIFAR-10 or Fashion-MNIST, using deeper networks with regularization and augmentation to improve image classification.

Explore sequence modeling and recurrent neural networks (RNNs), including hidden states and the vanishing gradient problem, with hands-on preprocessing and model building in TensorFlow and PyTorch.

Explore RNN architecture and backpropagation through time (BPTT), including input, hidden state, and output layers. Learn gradient challenges and solutions and implement RNNs for text classification with TensorFlow and PyTorch.

Explore how long short-term memory networks address RNN limitations with memory cells and input, forget, and output gates; apply to sentiment analysis using IMDb data and compare to vanilla RNN.

Explore gated recurrent units, a simpler LSTM variant with update and reset gates and fewer parameters for faster training, and learn through an IMDb GRU vs LSTM comparison.

Explore text preprocessing techniques, including tokenization, stemming, and lemmatization, and learn how word embeddings like glove and word2vec power an IMDb-based LSTM for sentiment analysis.

Explore rnn, lstm, and gru models for sentiment analysis, focusing on preprocessing, embedding, architecture variations, and hyperparameter tuning with metrics like accuracy and F1 score.

Understand how attention mechanisms overcome RNN limitations by focusing on relevant input parts. Explore core concepts—queries, keys, values—and self and multi-head attention, plus hands-on implementations in NumPy and PyTorch.

Explore the transformer architecture and its encoder–decoder structure, including self-attention, multi-head attention, positional encoding, and feedforward networks, with hands-on PyTorch and TensorFlow visualization.

Explore self-attention and multi-head attention in transformers, including QKV scoring, softmax weighting, and context vectors, with hands-on NLP exercises in machine translation, text summarization, and named entity recognition.

Discover how sinusoidal positional encoding lets transformers capture token order and generalize beyond training, and how feed-forward networks enhance representations in transformer layers.

Explore fine-tuning pre-trained transformers BERT and GPT for NLP tasks using Hugging Face, highlighting bidirectional versus unidirectional architectures, training objectives, and common applications like sentiment analysis and text generation.

Examine Bert variants—Roberta, Digital Bert, Albert, Bert Wheat—and GPT-3 capabilities, including zero- and few-shot learning, to perform natural language processing tasks; learn transfer learning and fine-tuning with Hugging Face.

Explore transformer based models for text summarization and translation, including T5 and BERT, with fine tuning, evaluation metrics like ROUGE and BLEU, and hands-on CNN daily mail data experiments.

master transfer learning with pre-trained models, enabling fine-tuning on small data for faster training and better generalization. apply resnet-50 in TensorFlow and PyTorch, freeze layers, and adapt the final layer.

Explore transfer learning in computer vision with pre-trained models like vgg, resnet, inception, and efficientnet, and learn freezing, unfreezing, and fine-tuning for image classification.

Fine-tune pre-trained computer vision models by freezing early layers and unfreezing late layers, apply data augmentation, and tune learning rate and batch size to improve generalization.

Learn transfer learning in NLP using pre-trained models such as Bert, GPT, T5, and Roberta, with tokenization, preprocessing, and fine-tuning for text classification, sentiment analysis, and summarization.

Explore discriminative fine tuning with layer-specific learning rates, warm-up and decay, and regularization with dropout, evaluated using F1 score and Bleu score for Bert and GPT.

Explore domain adaptation and transfer learning to tailor pre-trained models to domain-specific data, fine-tune with domain vocabularies, and address data mismatch and catastrophic forgetting with embeddings and data augmentation.

Fine-tune pre-trained models for a computer vision or NLP task using transfer learning. Apply regularization, data augmentation, and discriminative learning rates; evaluate with accuracy and F1, and document results.

Explore what AI agents are, their autonomy, perception, decision making, and learning, with hands-on development using Autogen, IBM, LangGraph, Crew AI, and AutoGPT.

This hands-on lecture introduces Autogen, a Microsoft open-source framework for multi-agent collaboration, enabling agent communication, tool use, code execution, and human-in-the-loop oversight, with Autogen Studio for low-code workflow deployment.

Learn to build scalable ai agents with the IBM Bee Agent framework, a model-agnostic open source platform for modular agents, tools, and observability.

Explore Lang Graph, a stateful, multi-agent framework in the Lang Chain ecosystem that orchestrates complex workflows with LLMs, human in the loop, and real-time streaming.

Explore Crew AI, a platform for building and deploying multi-agent AI systems with role-based agents, memory, and collaborative processes, deployable on-premise or in the cloud.

Explore Auto GPT, an experimental GPT-4 based framework that autonomously chains reasoning to complete tasks, with steps to install prerequisites, configure API keys, set goals, and monitor progress.

Explore evolution and importance of MLOps, including versioning, automation, and monitoring. Compare MLOps with DevOps and build an MLOps project structure using Git and Docker with a simple model pipeline.

MLOps integrates machine learning with DevOps to automate and streamline the ML model lifecycle from development to deployment. It enables collaboration to ensure scalable, reproducible, and maintainable models in production.

Trace the evolution of machine learning operations (MLOps) from manual development to automated, scalable workflows, emphasizing continuous integration and delivery, deployment, monitoring, and reproducibility.

Explore core MLOps concepts: versioning across data, models, and code; automate training and deployment with CI/CD pipelines; and monitor performance, concept drift, and logs.

Examine MLOps versus DevOps, highlighting similarities in automation, collaboration, and CI/CD pipelines, and differences in artifacts, experimentation, and monitoring for models versus systems.

Explore the data science to production pipeline, from data preparation to deployment, and compare experimentation versus production while addressing challenges in deploying ML models through an end-to-end hands-on pipeline.

Navigate the machine learning workflow from data collection to deployment in production. Explore data pre-processing, feature engineering, training, evaluation with metrics, tuning, deployment, and monitoring.

Compare experimentation and production in ML, from jupyter notebooks and small data for feature engineering to scalable pipelines with kubeflow and ci cd for automated retraining.

Explore scalability and infrastructure challenges in deploying ML models, including real-time serving, reproducibility across environments, reliability under varying loads, and concept drift.

Build an end-to-end machine learning pipeline in Python and Jupyter notebooks, from data pre-processing and model training to evaluation, saving, and loading a California housing price prediction model.

Explore the infrastructure essentials for MLOps, including cloud platforms AWS, GCP, and Azure, containerization with Docker, and Kubernetes orchestration, plus hands-on setup of a local MLOps environment.

Compare AWS, GCP, and Azure for MLOps, with services like SageMaker, Lambda, S3, AI Platform, BigQuery ML, GKE, and Azure ML, and learn setup steps.

Leverage Docker containerization to package ml model code and dependencies into portable images, ensuring reproducibility and scalable deployment across development, testing, production, and AWS, GCP, and Azure.

Orchestrate machine learning workloads with Kubernetes by deploying, scaling, and managing containerized apps through pods, nodes, deployments, and services.

Set up local ml ops environments by isolating dependencies with python virtual environments, sqlite data storage, and docker compose; implement unit and integration tests, version control, and automated builds.

Set up Docker and Kubernetes environments to containerize a simple ML model, deploy it locally with Minikube, and expose a Flask API for testing.

Master model tuning, deep learning, transformers, ai agents, and mlops to deploy real world ai systems. Harness your ai engineer certificate as a launchpad for your future.