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AI Engineering Training Program

A practical course designed to help learners understand how modern artificial intelligence systems are built and used in software projects. The program introduces machine learning fundamentals, model evaluation, data preparation, and common workflows for integrating AI features (including LLM-based features) into real applications.

Course overview

This program focuses on engineering workflows rather than marketing claims. You will learn how to approach AI work step-by-step: defining the problem, preparing data, selecting a baseline model, evaluating performance, debugging errors, and deploying a simple AI-enabled feature behind an API. We also introduce responsible AI topics such as privacy, safety, and basic risk checks.

Note: This training is educational. It does not guarantee certification, employment, income, or any specific outcome.

Topics covered

Python for AI work: environments, notebooks, data handling practices
Machine learning basics: regression, classification, overfitting, generalization
Evaluation: train/validation splits, metrics, error analysis, sanity checks
Data preparation: cleaning, feature engineering, handling missing values
Model iteration: baselines → improvements → measurement and documentation
LLM workflows (intro): prompting patterns, structured outputs, reliability basics
RAG basics (intro): retrieval + generation overview, simple indexing concepts
Integration: API patterns, latency awareness, cost awareness, monitoring basics
Responsible AI: privacy considerations, safety constraints, common risk scenarios

Tools & technologies

The course uses widely adopted tools and libraries so learners can apply the skills to different environments.

Core

Python, Jupyter, NumPy, Pandas, scikit-learn

Workflow

Git basics, reproducible experiments, simple documentation patterns

Integration

REST API concepts, basic request/response design, deployment awareness

Optional

Docker fundamentals (intro), lightweight monitoring and logging concepts

Who this is for

This program is suitable for developers, technical professionals, and learners who want a structured introduction to AI engineering. Basic programming familiarity is helpful, but the course starts from fundamentals and builds up using practical examples.

If you are a complete beginner, you can still follow along—expect to spend extra time on Python basics and practice.

Example learning path

Below is an example structure to show what “practical AI engineering” means in this program.

Module 1

AI concepts, Python setup, data handling workflows

Module 2

ML fundamentals: baselines, metrics, evaluation habits

Module 3

Data prep + feature engineering + iteration and debugging

Module 4

Intro to LLM workflows + reliability basics + structured outputs

Module 5

RAG basics: retrieval overview + simple integration patterns

Module 6

Deploying an AI feature: API patterns, latency/cost awareness, monitoring basics

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Privacy & usage

We only use the provided email address to send course information and updates about the training program. You can unsubscribe at any time.

Do you guarantee outcomes?

No. This training is for educational purposes only. It does not guarantee certification, employment, income, or any specific result. Progress depends on your background and practice time.