Introduction to Machine Learning and AI Engineering

AI initiatives create risk when teams can prompt tools but cannot evaluate model behavior, protect sensitive data, or explain failures. This 26.9-hour beginner course gives IT Directors and Training Managers a structured way to onboard programmers, developers, data analysts, IT Practitioners, and DevOps engineers with basic coding experience into practical ML and AI engineering workflows. Teams build shared vocabulary across AI, ML, deep learning, and GenAI, then move into regression, tensors, PyTorch, TensorFlow, LLM APIs, RAG, evaluation, and agent safety. For change management, assign it as a foundation before approving production AI work so teams align on privacy cautions, model uncertainty, testing, and escalation paths. CBT Nuggets Playlists can sequence the course for role-based cohorts, and Team Reporting helps Team Leads track progress across the full learning path.

IT teams complete this training to move from AI experimentation to more testable, governed workflows. Course scenarios include image classification uncertainty, safe use of enterprise LLM accounts, tensor debugging, RAG apps with Chroma and LangChain, and agent evaluation.

• Reduce AI data-handling risk by reinforcing cautions around PII, PHI, personal accounts, enterprise accounts, connectors, and organizational policies.
• Improve reliability by teaching teams to evaluate outputs, inspect failed cases, and avoid “run it and hope it works” AI delivery.
• Shorten troubleshooting cycles by giving practitioners a working model of loss functions, gradients, optimizers, tensors, and training loops.
• Support safer AI agents with limited tools, validated arguments, stop conditions, trace logging, escalation, human review, and eval datasets.

Knowledge

• Differences between AI, machine learning, deep learning, and generative AI.
• How regression, classification, perceptrons, tensors, and neural networks fit into ML systems.
• Why loss functions, metrics, gradients, optimizers, and training loop order matter.
• How PyTorch and TensorFlow support model building for structured data, images, and text.
• How LLM APIs, token costs, prompt engineering, RAG, tools, and agents shape AI engineering work.

Ability

• Build introductory PyTorch models for linear regression, logistic regression, non-linear networks, and image classification.
• Create TensorFlow NLP workflows, including text classification and evaluation.
• Analyze tensor shape issues that cause PyTorch errors.
• Build a RAG application using Chroma and LangChain.
• Evaluate AI outputs, automate evaluation workflows, and inspect failures in agent behavior.