AI Models Courses and Certifications

Coursera

Building and Optimizing AI Models

Train and validate predictive models, design custom CNN, RNN and Transformer architectures, fine-tune pretrained models, and benchmark accuracy, latency and memory with Python and TensorFlow.

• Coursera
• 8 weeks, 1 hour a week
• Self-Paced
• Paid Course

Alberta Machine Intelligence Institute

Building and Deploying Generative AI Models

Build Transformer architectures from scratch in PyTorch, engineer RAG pipelines with LangChain, and deploy a summarizer AI agent to Vertex AI with monitoring.

• Coursera
• 3 weeks, 5 hours a week
• Self-Paced
• Paid Course

Whizlabs

Prompt Engineering Generative AI & LLM Models Fundamentals

Learn how LLMs are trained, aligned, and evaluated, then design effective prompts and apply fine-tuning, prompt tuning, PEFT, and Retrieval-Augmented Generation.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Alberta Machine Intelligence Institute

Generative AI for Audio and Images: Models and Applications

Explore how VAEs, VQ-VAEs, GANs, transformers, and diffusion models generate audio, images, and video, training GAN models and weighing deepfakes, bias, and intellectual property.

• Coursera
• 4 weeks, 7 hours a week
• Self-Paced
• Paid Course

Coursera

Preparing Text for AI Models

Build production-ready text datasets for LLM training: source data from Hugging Face, Kaggle, and Common Crawl, then clean, tokenize, annotate, and split it.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Whizlabs

Azure AI & ML: Optimize Language Models for AI Applications

Deploy and optimize models on Azure: RAG and fine-tuning in AI Foundry, prompt variant testing, AI Search indexes, MLflow tracking, and production ML pipelines.

• Coursera
• 2 weeks, 4 hours a week
• Self-Paced
• Paid Course

Programming Generative AI: Image and Language Models

Build generative models for images and text: image representation, CNNs, variational autoencoders, diffusion models with Hugging Face diffusers, NLP pipelines, and transformers in PyTorch.

• Coursera
• 8 hours 26 minutes
• Self-Paced
• Paid Course

Learning Deep Learning: Generative AI and Large Language Models

Build deep learning models with BERT, GPT, R-CNN, U-Net and Mask R-CNN in TensorFlow and PyTorch, covering multimodal networks, multitask learning, object detection, segmentation and AI ethics.

• Coursera
• 6 hours 29 minutes
• Self-Paced
• Paid Course

Coursera

Architect AI Solutions: From Needs to Models

Translate stakeholder requirements into AI architectures: map needs to managed APIs, cloud AI services or custom models, and design solutions with vector databases and orchestration layers.

• Coursera
• 1 hour 56 minutes
• Self-Paced
• Paid Course

Coursera

Preparing Images for AI Models

Source, clean, and augment image datasets for diffusion model training: evaluate LAION and ImageNet, apply resizing, normalization, and metadata checks, then MixUp, CutMix, and style transfer.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Pragmatic AI Labs

Agentic AI: Actor Models and Subagent Architecture

Build fault-tolerant agentic AI systems on actor model principles: Actix supervision trees in Rust, Go goroutine supervisors, Deno actors, and Claude subagent delegation.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Fine-Tune & Optimize Generative AI Models

Tune decoding strategies (temperature, top-k, beam search), evaluate outputs with BLEU, ROUGE and BERTScore, and apply LoRA parameter-efficient fine-tuning using Hugging Face Transformers and PEFT.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Secure AI Interpret and Protect Models

Harden AI models against evasion, data poisoning, and model extraction using input sanitization, adversarial training, differential privacy, and red-teaming exercises in a hands-on lab.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Deploy and Scale AI Models with Cloud Run

Deploy gen AI apps and machine learning models for AI inference on Cloud Run, using GPUs and integrating with data storage services.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Board Infinity

Generative AI: Fine-Tuning LLMs and Diffusion Models

Fine-tune 7B+ LLMs with LoRA, QLoRA and SFT pipelines, dissect decoder-only transformers and KV caching, and generate controllable images with diffusers and ControlNet.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course