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L-SYSTEM

intermediate

Applied AI for Big4 IT Professionals

A practical AI course for IT professionals at Big4 and other professional-services firms. Not data-science training: you learn to choose the right AI approach, measure quality, cost and risk, and design a PoC that can honestly say no. The whole course follows one running case. You work in the Digital & IT team of the fictional firm Aldervane Advisory LLP and run two pilots: a Triage PoC that suggests and partly automates routing of ENGAGE Service Desk tickets, and a Policy Assistant, a RAG assistant over internal IT policies that later gets limited agent actions. The data is synthetic but carries real-world defects: leakage in the ticket export, a superseded policy version in the index, a restricted client folder. Topics: framing AI use cases; optimization; probability, base rates and Bayesian updating; classical ML (regression, classification, similarity, triage); enterprise documents; evaluation (precision, recall, overfitting, shadow mode, monitoring); neural networks without magic; LLMs and adaptation options; RAG; AI agents, least privilege and prompt injection; Responsible AI and governance, including the EU AI Act, NIST AI RMF and ISO/IEC 42001 as of September 2026; business cases and go / iterate / stop criteria; a capstone proposal. Every topic is taught as pain, solution and cost. About 190 practice items including calculations, diagrams and illustrations in every unit, and three optional Python labs with ready-made data generators: workload forecasting, ticket classification and a RAG prototype. Course language: Russian.

Your lesson is in your language: your assistant leads it.

  • Material: Russian
  • Exam: Russian — taken on the site, not translated
How it works

Your assistant takes the course material from our server and leads the lesson in your language.

The exam is taken on the site, without an assistant. Exam questions are not translated: this is decided for every course.

How to connect an assistant

Path through the course

· 19 units

the order is free · the steps below are the order the author suggests, not a conditionView: map · steps

  1. Stage 1

    ~1.5 h

  2. Stage 2

    ~5 h

    by the author’s design, after “AI in Professional Services: from task to value”

  3. Stage 3

    ~7 h

  4. Stage 4

    ~6 h

  5. Stage 5

    ~1.5 h

  6. Stage 6

    ~2.5 h

  7. Stage 7

    ~2.5 h

  8. Stage 8

    ~7.5 h

  9. Stage 9

    ~2 h

    • 15 · by the author’s design, after “Model Evaluation, overfitting and business metrics”, “Responsible AI, privacy, security and governance”

      AI Business Case: from idea to PoC

      ~2 h

  10. Stage 10

    ~4 h

    • 16 · by the author’s design, after 13 of these: “AI in Professional Services: from task to value”, “Optimization: when AI searches for the best solution”, “Probability and uncertainty”, “Bayesian reasoning: updating confidence on evidence”, “Machine Learning Fundamentals”, “Regression and forecasting”, “Classification, similarity and triage”, “Working with text and enterprise documents”, “Model Evaluation, overfitting and business metrics”, “Neural Networks and Deep Learning without magic”, “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”, “AI Agents and workflow automation”, “Responsible AI, privacy, security and governance”, “AI Business Case: from idea to PoC”

      Capstone: AI Solution Proposal

      ~4 h

  11. Exam

    closed · enrol on the course

    • final · in the browser, without the assistant

      Exam

The path in words
  • Stage 1 · AI in Professional Services: from task to value — ~1.5 h
  • Stage 2 · Optimization: when AI searches for the best solution — by the author’s design, after “AI in Professional Services: from task to value”; ~1.5 h
  • Stage 2 · Probability and uncertainty — by the author’s design, after “AI in Professional Services: from task to value”; ~1.5 h
  • Stage 2 · Machine Learning Fundamentals — by the author’s design, after “AI in Professional Services: from task to value”; ~2 h
  • Stage 3 · Bayesian reasoning: updating confidence on evidence — by the author’s design, after “Probability and uncertainty”; ~1.5 h
  • Stage 3 · Regression and forecasting — by the author’s design, after “Machine Learning Fundamentals”; ~1.5 h
  • Stage 3 · Classification, similarity and triage — by the author’s design, after “Machine Learning Fundamentals”; ~2 h
  • Stage 3 · Working with text and enterprise documents — by the author’s design, after “Machine Learning Fundamentals”; ~2 h
  • Stage 4 · Model Evaluation, overfitting and business metrics — by the author’s design, after “Regression and forecasting”, “Classification, similarity and triage”; ~2 h
  • Stage 4 · Optional Lab: Regression in Python — by the author’s design, after “Regression and forecasting”; optional; ~2 h
  • Stage 4 · Optional Lab: Text Classification in Python — by the author’s design, after “Classification, similarity and triage”; optional; ~2 h
  • Stage 5 · Neural Networks and Deep Learning without magic — by the author’s design, after “Model Evaluation, overfitting and business metrics”; ~1.5 h
  • Stage 6 · Generative AI and LLMs in the enterprise — by the author’s design, after “Neural Networks and Deep Learning without magic”; ~2.5 h
  • Stage 7 · RAG and Enterprise Knowledge — by the author’s design, after “Generative AI and LLMs in the enterprise”; ~2.5 h
  • Stage 8 · AI Agents and workflow automation — by the author’s design, after “RAG and Enterprise Knowledge”; ~2 h
  • Stage 8 · Responsible AI, privacy, security and governance — by the author’s design, after “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”; ~2.5 h
  • Stage 8 · Optional Lab: RAG Prototype — by the author’s design, after “RAG and Enterprise Knowledge”; optional; ~3 h
  • Stage 9 · AI Business Case: from idea to PoC — by the author’s design, after “Model Evaluation, overfitting and business metrics”, “Responsible AI, privacy, security and governance”; ~2 h
  • Stage 10 · Capstone: AI Solution Proposal — by the author’s design, after 13 of these: “AI in Professional Services: from task to value”, “Optimization: when AI searches for the best solution”, “Probability and uncertainty”, “Bayesian reasoning: updating confidence on evidence”, “Machine Learning Fundamentals”, “Regression and forecasting”, “Classification, similarity and triage”, “Working with text and enterprise documents”, “Model Evaluation, overfitting and business metrics”, “Neural Networks and Deep Learning without magic”, “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”, “AI Agents and workflow automation”, “Responsible AI, privacy, security and governance”, “AI Business Case: from idea to PoC”; ~4 h

Programme · 19 units

1AI in Professional Services: from task to value

Distinguish an AI task from ordinary automation and analytics

~1.5 h
2Optimization: when AI searches for the best solution

Understand optimization as choosing the best action under constraints

~1.5 h
3Probability and uncertainty

Interpret a model's probability as a share over a set of cases and understand calibration

~1.5 h
4Bayesian reasoning: updating confidence on evidence

Understand prior, likelihood and posterior, and calculate the update through odds and the likelihood ratio

~1.5 h
5Machine Learning Fundamentals

Distinguish supervised (classification, regression), unsupervised and generative problem settings

~2 h
6Regression and forecasting

Understand regression as predicting a number and forecasting as regression over time

~1.5 h
7Classification, similarity and triage

Distinguish binary, multiclass and multilabel classification

~2 h
8Working with text and enterprise documents

Distinguish extraction, classification, retrieval, summarization and generation, and match each with its own check

~2 h
9Model Evaluation, overfitting and business metrics

Distinguish train, validation and test, and recognise overfitting and test set wear-out

~2 h
10Neural Networks and Deep Learning without magic

Understand layer, parameters, loss and gradient descent, and calculate the number of parameters in a layer

~1.5 h
11Generative AI and LLMs in the enterprise

Understand an LLM as a probabilistic generative model and explain the nature of hallucination

~2.5 h
12RAG and Enterprise Knowledge

Trace the RAG pipeline from ingestion to a grounded answer and name where in it the answer can break

~2.5 h
13AI Agents and workflow automation

Distinguish an agentic workflow from a chatbot and from a deterministic workflow, and choose between them

~2 h
14Responsible AI, privacy, security and governance

Recognise the main categories of AI risk in specific systems

~2.5 h
15AI Business Case: from idea to PoC

Fill in a use case canvas and assess data, technical and operational feasibility

~2 h
16Capstone: AI Solution Proposal

Synthesise business, data, model, architecture and governance decisions in one proposal

~4 h
17Optional Lab: Regression in Python

Go through the full cycle of a simple regression experiment from baseline to report

~2 h
18Optional Lab: Text Classification in Python

Build a baseline text classifier on synthetic tickets and compare it with a trivial baseline

~2 h
19Optional Lab: RAG Prototype

Build minimal retrieval for RAG on a safe synthetic knowledge base

~3 h