Six modules — from first principles to a live optimization loop
Hour 1 builds the AI. Hour 2 plugs it into the network. Hour 3 opens the real Ericsson counter files. Hour 4 fixes a real degradation. Then two deep-dive chapters open the AI engine all the way. Lesson 1 is free to watch right now.
The Learning Machine
What AI actually is — every model family opened up with real telecom numbers: regression by hand, gradient descent, trees & forests, k-means, neural nets, attention, LLMs, reinforcement learning, and the discipline that separates a demo from a deployment.
AI Meets the Network
Where AI plugs in: the PM/CM/FM data estate, SON, NWDAF (TS 23.288), the O-RAN RICs with rApps & xApps, the Ericsson stack (EIAP, ENM, MicroSleep Tx), and the deployment discipline — shadow mode, MLOps, SHAP, guardrails.
The KPI Engine — Ericsson, Verbatim
Real counters, real formulas, animated: DRB Accessibility as a product of three doors (→ 97.0%), Retainability with its sacred active qualifier (→ 0.52%), the five-child drop-cause tree — then five detectors racing the same degradation.
Closing the Loop — Drop-Rate Surgery
The five verbs on a real cell: the SHAP evidence ledger, PIM-vs-external forensics, a verified recovery 1.81% → 0.33% against control cells — plus MRO tuning, sleeping cells, RL energy saving, load balancing and the Python pipeline.
Deep Learning Unpacked
The neural network bolt by bolt — every number by hand. Build a neuron from arithmetic, run the forward pass by hand, compute backpropagation on paper, then CNNs, LSTMs and embeddings. A black box no more.
Generative AI, LLMs & Agents
Modern AI, opened up: tokenization, embeddings, the attention mechanism worked by hand, the transformer block, next-token pretraining, alignment, RAG, and AI agents that use tools — the complete arc from a neuron to an agent.
Walk out able to read the network — and fix it with AI
Three interactive tools built into the course
You don't just watch — you drive. Every tool is a working simulator or analyzer, computed live in your browser.
A live NOC console
A 12-cell network writing real Ericsson counters. Inject a fault — PIM, mobility, a sleeping cell — advance the clock, and watch the counters climb, the AI detect and diagnose it, and your fix recover the drop rate against a control cell. With an autonomous fleet-sweep work queue.
Six real case studies
Fully-worked, engineering-grade studies of AI/ML optimization in production — fleet-scale PIM detection, RL energy saving, sleeping-cell autoencoders, MRO bandits, capacity forecasting, interference fingerprinting — each with data, model, method, before/after and the catch.
Dissect the models like packets
A Wireshark-style analyzer of the 15 model families: a display filter, a packet list, an expandable dissection tree, and a live mechanism animation for every model — watch the regression line fit, the sigmoid squash, the attention arcs thread.
Built for the engineer who has to make it work
Everything, for the price of a coffee
No subscription. One payment, lifetime access, and lesson 1 is free so you can see the quality before you spend a rupee.
Abhijeet Kumar
A hands-on telecom engineer who builds real optimization tools, labs and courses on live networks. This masterclass distills years of RAN optimization and applied AI into one thing: teaching you to read a network's own data and answer it — grounded in real Ericsson counters, not theory.
Questions, answered
Is the first lesson really free?+
Do I need to be an AI expert already?+
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Teach the network to optimize itself.
Six modules, a live lab, real case files — the complete, hands-on path from AI theory to real Ericsson KPI optimization. Start free, unlock everything for the price of a coffee.