Lazarina Stoy
Marketing & Machine Learning Consultant, Trainer & Speaker · MLforSEO
Lazarina Stoy is a marketing and machine learning consultant, trainer, and speaker, and the founder of MLforSEO.
About
Lazarina Stoy is a marketing and machine learning consultant, trainer, and speaker, and the founder of MLforSEO — a training platform and community that helps organic search marketers put machine learning to work in their day-to-day. Over more than a decade in digital marketing and data, she has become known for a practical, sustainable, and scalable approach to applying AI and ML to SEO.
Lazarina has partnered with enterprise-level organizations via her work in various agencies — including AWS, Skyscanner, Compass, and Extreme Networks — to strengthen their organic search strategies, implement process automation, and drive sustainable growth, frequently using machine learning to execute ambitious strategies at scale.
Beyond consulting, she is a dedicated educator. Through MLforSEO she has produced a large body of training resources, courses, Looker Studio dashboard templates, and Google Sheets tools, and she is a regular speaker and webinar guest at leading digital marketing and SEO events. Her work has appeared in major industry publications such as Search Engine Land and Moz.
Lazarina is also the co-founder of Women in Marketing – Bulgaria, a community supporting local marketers through education and events. Whether she is consulting, teaching, or speaking, her focus is the same: making machine learning approachable, ethical, and genuinely useful for marketers who want to work smarter.
Expertise
Lazarina specializes in applying machine learning to organic search — entity-based SEO, AI Search and generative results, technical SEO automation, and marketing measurement — with an emphasis on practical, scalable workflows.
Professional experience
10+ years across SEO, data analysis, and marketing automation. Has worked with enterprise teams including AWS, Skyscanner, Compass, and Extreme Networks. Contributor to leading industry publications including Search Engine Land and Moz. Regular conference speaker and webinar guest. Creator of extensive training resources, courses, and Looker Studio templates through MLforSEO.
What Is Query Augmentation in SEO? How Search Engines Expand Queries (and Why It Matters Now)
What Is a Knowledge Graph? How to Use It for Smarter Keyword Strategy
What Is Information Gain in SEO? How Google Measures Content Uniqueness
What Are Synthetic Queries? Why They Matter for Semantic SEO and AI Search
What Is SERP Feature Analysis? How to Use It for Smarter Keyword Research
What Is Search Intent in SEO? A Practical Guide to Using It in Semantic Keyword Research
How to Structure and Categorise Keyword Data for Semantic Analysis
How to Turn Semantic Keyword Research Into an Actionable SEO Workflow
What Is the EAV Model in SEO? A Practical Framework for Semantic Keyword Research
What Is Implicit User Feedback in SEO? How Behavioural Signals Shape Search Rankings
What Does a Good Semantic Keyword Universe Actually Look Like?
What is Query Context and Session Context in SEO?
What is Search Query Sequence and Query Path?
404 and Redirect Mapping with Fuzzy Matching in Google Colab (Python)
Different ways to Map Keywords to topics – with Supervised and Unsupervised ML approaches
How to use Google Autocomplete API and Places API for Keyword Suggestions with Python
How to do keyword clustering with KeyBERT
How to transcribe audio with OpenAI’s Whisper API in Google Colab (Python)
How to do content moderation with Google’s Natural Language API in Google Sheets (Apps Script)
How to do Syntax Analysis with Google’s Natural Language API in Google Sheets (Apps Script)
How to do Sentiment Analysis with Google’s Natural Language API in Google Sheets (Apps Script)
How to do Entity Extraction with Google’s Natural Language API in Google Sheets (Apps Script)
How to do Text Classification with Google’s Natural Language API in Google Sheets (Apps Script)
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Search Intent Classification from SERP Features in Python: Rule-Based Notebook for SEO
Semantic Search Intent Classification with Google Cloud NLP & spaCy in Python (Colab Notebook)
Transformer-Based Search Intent Classification with BART Zero-Shot & SERP Signals (Colab Notebook)
Customer Segmentation with Machine Learning Using Three Clustering Approaches
Image Clustering by Color with Three Machine Learning Approaches for Visual Content Organization
Synthetic vs User Query Classification Notebook with Rule-Based and Machine Learning Detection
Entity Analysis with with Google Cloud Natural Language API (No-Code Template)
Looker Studio Text Classification Dashboard Template with Interactive Category Visualization and Hierarchical Analytics
IBM Watson NLU Sentiment and Emotion Analysis Template for Google Sheets with Multi-Dimensional Affect Detection
IBM Watson NLU Complete API Implementation Template for Multi-Feature Text Analysis (Notebook)
Comprehensive Content Brief Template with Data Source Mapping for Semantic Keyword Research Implementation
Sentiment Analysis No-code Template with Google Cloud Natural Language API
Google Sheets Content Moderation Template with Google Cloud Natural Language API for Toxic Content Detection
Google Sheets Syntax Analysis Template with Google Cloud Natural Language API for Grammatical Structure Parsing
Google Sheets Text Classification Template with Google Cloud Natural Language API for Automated Content Categorization
Multi-Platform Autocomplete Keyword Research with Google Search, YouTube, and Places APIs (Notebook)
Transitioning from Traditional to Semantic Keyword Universe (Checklist)
Knowledge Graph API Extraction and Entity Relationship Analysis for Semantic Keyword Research (Notebook)
Map Keywords to Topics: Supervised (sBERT + Fuzzy) vs. Unsupervised (BERTopic) (Notebook)
Identifying Desired Content Formats & Platforms from SERP Data (Lab Notebook)
Comprehensive String Matching & Fuzzy Matching Reference Guide for SEO Applications: Use Cases, Approaches, Models
Automated Redirect Mapping with Triple Fuzzy Matching for Site Migrations (Notebook)
Mapping 404 URLs to Live Pages with Fuzzy Matching (Guide)
Calculating Semantically Similar Terms with Fuzzy Matching for Keyword Clusers (Notebook)
Fuzzy Matching: Map Keywords to Seed Terms or Topics (Notebook)
Advanced Entity Analysis with Multiple ML Techniques for Semantic Keyword Research
Comprehensive Customer Review Semantic Analysis with Google Cloud NLP & IBM Watson NLU
Customer Reviews Semantic Analysis: Looker Studio Template
ML-Powered Internal Linking Opportunity Discovery with LinkBERT
Entity & Sentiment Analysis APIs: Comparative Review
Entity Analysis & Relationship Mapping with IBM Watson NLU in Google Colab
Extracting Entities with Google Cloud Natural Language API in Python (Tutorial)
BERTopic: Benefits and Limitations
Complete Guide to Entity Extraction & Sentiment Analysis: Marketing and SEO Use Cases
Complete Guide to ML Clustering for Marketing & SEO: Use Cases & Model Choices
KeyBERT for SEOs: Google Colab Template
Web Content Clustering: Topic Modeling with LDA (Notebook)
Web Content Clustering: Topic Modeling with BERTopic (Notebook)
Text Classification in Google Sheets with Google Cloud Natural Language API
ML Data Characteristics Mapped to SEO & ML Tasks (Guide)
When to use Machine Learning in SEO – Decision Checklist
Complete Guide Understanding ML Classification for Marketers: Marketing Use Cases & Model Choices
Text Classification with Google Cloud Natural Language API in Python (Google Colab Notebook)
Keyword Cluster (KeyBERT) Looker Studio Demo Visualization Dashboard for SEO
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Introduction to Machine Learning for SEO
Apply ML models — clustering, classification, entity extraction, and fuzzy matching — to automate SEO workflows like keyword grouping, redirect mapping, and content scaling. No coding background required.
View courseSemantic ML-enabled Keyword Research
Master entities, search intent, query analysis, and knowledge graphs to build a semantic keyword universe — with practical scripts and ML APIs that move beyond traditional keyword targeting.
View courseOne of the best training sessions I have ever attended. Lazarina was so organized, provided us with a lot of training/template material and was always so engaging while explaining everything.
We know now what to do as a department. Lazarina’s knowledge, presentation skills and frankly organisation are shockingly good. Not to mention how easy to work, approachable and available she is. Detailed, with context, outcomes and question sessions that totally surpassed anything any of us had in mind when we woke up that day for the training.
ML can be a dense and complicated subject, but Lazarina explains everything in a way that feels engaging and accessible. Not overwhelming but also not watered down. She is great at breaking things down clearly, which makes the subject far less intimidating.
One of the best courses I’ve bought and is helping me stay relevant as an SEO. There’s crazy amounts of value, and Lazarina shares a ton of info.
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