Machine Learning Mastery
Machine Learning Mastery is a privately held educational publisher selling a 33-title catalog of PDF eBooks and free blog tutorials that teach software developers applied machine learning, from classical algorithms through deep learning and transformer training, via direct-to-consumer digital download.
- Company typePrivate
- Founded2013
- HeadquartersMelbourne, Australia
- Headcount1–10
- GTM typeB2C
- OfferingDigital Commerce or Content
What Machine Learning Mastery does
Machine Learning Mastery is a privately held educational content publisher that produces a catalog of PDF eBooks and free blog tutorials teaching applied machine learning to software developers. The catalog spans 33+ titles covering foundational mathematics (statistics, linear algebra, probability, calculus, optimization), classical ML algorithms (scikit-learn, XGBoost, ensembles), deep learning with Keras and PyTorch, and advanced topics including NLP, computer vision, GANs, time series forecasting, and transformer training. Each eBook follows a uniform playbook format with accompanying Python code and datasets, designed to be read with a code editor open rather than as passive reference material. Free blog tutorials and email-based mini-courses function as top-of-funnel acquisition, converting readers into paid book purchases.
The company was founded in 2013 by Jason Brownlee, Ph.D., who is both primary author and operator, supported by a small editorial team including an Editor in Chief and technical editors. The legal entity is Zeus LLC, registered in Puerto Rico (Company Number 421867-1511), with the company operating under the umbrella of parent Guiding Tech Media. All operations are digital and remote, with global reach via direct download from machinelearningmastery.com.
Revenue is generated almost exclusively through direct-to-consumer eBook sales on the company website. Individual titles are priced at $37 for a perpetual, DRM-free PDF license that includes free lifetime updates for API changes and bug fixes, and a 33-book Super Bundle is sold for $297 versus an aggregate individual value of $1,231. Payments are accepted via PayPal and credit card; a 90-day money-back guarantee, student and teacher discounts, and email-driven promo codes are offered. Distribution is entirely self-service and direct via the company's own storefront, with no third-party resellers, marketplace presence, or enterprise sales motion. The customer base is horizontal and consumer-grade: individual developers, students, career changers, and professionals seeking practical ML skills.
Machine Learning Mastery firmographics
Firmographics- Name
- Machine Learning Mastery
- Legal name
- Zeus LLC
- Website
- https://machinelearningmastery.com
- Company type
- Private
- Founded year
- 2013
- Operating status
- Operating
- Headcount range
- 1–10 employees
- Short description
- Machine Learning Mastery is a privately held educational publisher selling a 33-title catalog of PDF eBooks and free blog tutorials that teach software developers applied machine learning, from classical algorithms through deep learning and transformer training, via direct-to-consumer digital download.
- Ownership category
- akta.pro rank
Machine Learning Mastery industry classification
Industry- Product category
- Machine Learning Education
- NAICS
- Business Schools and Computer and Management Training (6114), Computer Training (611420), Software Publishers (5132)
- SIC
- Books: Publishing Or Publishing & Printing (2731), Services-Educational Services (8200)
- akta.pro primary industry
- Online Learning Content & Course Publishing Platforms (BPAMAJAL)
- akta.pro secondary industries
- Creator Course Marketplaces (Multi-Instructor Catalogs) (MPACALAA), Core Digital Curriculum & Courseware (Full Programs & Units) (EDAFACAA), Learning Content Bundling & Subscription Passes (All-Access Libraries) (MPACALAL)
Keywords
Where Machine Learning Mastery is headquartered
LocationHeadquarters
- HQ city
- Melbourne
- HQ country
- Australia
- HQ region
- Oceania
Offices1 record
Markets served
Machine Learning Mastery business model
Business model- GTM type
- B2C
- Offering type
- Digital Commerce or Content
- Cost components
- Personnel, Marketing or Sales, Operations, Technology or R&D, Infrastructure
Revenue model
- eBook Sales (Individual): Single PDF eBooks sold for one-time purchase at $37 each. Customers receive immediate download after purchase with free updates for bug fixes and API changes.
- eBook Bundles: Pre-configured themed bundles offering discounted pricing. Super Bundle offers 33 books at $297 (vs $1,231 individual price), representing $934 savings. Bundles are fixed collections with no customization or exchanges supported.
- Discount Coupons: Occasional promotional discounts offered to past customers and email subscribers. Currently offering 20% off with code '20offearlybird' on select titles.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| One time/ perpetual license | Pay-as-you-go | Single eBook - $37 per book |
| One time/ perpetual license | Pay-as-you-go | Super Bundle - 33 books for $297 |
| One time/ perpetual license | Pay-as-you-go | Promotional Discount - 20% off |
| One time/ perpetual license | Multi-year contract | Educational Discount - Contact for pricing |
Go-to-market motion2 records
Distribution channels1 record
Marketing channels5 records
Machine Learning Mastery product offering
Product offeringCore offering
Machine Learning Mastery operates an online educational content platform that sells PDF eBooks and free tutorials teaching applied machine learning to developers. The catalog includes 33+ eBooks covering foundational mathematics, classical ML algorithms, deep learning frameworks (Keras, PyTorch), and advanced topics like transformers, NLP, computer vision, GANs, and time series forecasting. Products are sold individually at $37 each or as themed bundles (e.g., the Super Bundle of 33 titles for $297) via the company's own e-commerce storefront with immediate digital download.
Product overview
Machine Learning Mastery is an educational content platform offering a portfolio of 33+ ebooks and free tutorials for developers learning applied machine learning. The core offering consists of individual ebooks organized into thematic bundles, with the flagship Super Bundle containing all 33 titles. Products span foundational mathematics (statistics, linear algebra, probability, calculus, optimization), traditional ML algorithms (XGBoost, ensemble methods), deep learning frameworks (Keras, PyTorch), and advanced topics (transformers, NLP, computer vision, GANs, time series). The company also provides free blog tutorials and mini-courses as lead generators for the ebook catalog.
Differentiator
Problem solved
Functional benefit
Products and services
- Super Bundle (33-Ebook Set) Complete collection of 33 ebooks covering foundational mathematics (statistics, linear algebra, probability, calculus, optimization), machine learning algorithms, deep learning (Keras/PyTorch), NLP, computer vision, time series, transformers, GANs, XGBoost, and practical applications. Valued at $1,231 individually, priced at $297 with free lifetime updates.
- Training Transformer Models From Scratch with PyTorch Practical guide to training transformer models from scratch with PyTorch, covering BERT (encoder-only) and Llama/GPT (decoder-only) architectures. Includes 19 chapters on datasets, tokenizer training, pretraining, fine-tuning, and multi-GPU distributed training (data parallelism, pipeline parallelism, FSDP, tensor parallelism).
- Machine Learning with Python Comprehensive guide to applied machine learning using Python with scikit-learn, covering algorithms, data preparation, and end-to-end predictive modeling projects.
- Deep Learning with Python Guide to deep learning using Keras/Python covering MLPs, CNNs, RNNs, and training best practices for neural network models.
- Master Machine Learning Algorithms Teaches how top machine learning algorithms work using worked examples in arithmetic and spreadsheets without code. Focuses on understanding how models learn and make predictions.
- Deep Learning with PyTorch Guide to deep learning using PyTorch covering tensors, neural networks, CNNs, LSTMs, and model training with the PyTorch framework.
- NLP with Hugging Face Transformers Covers natural language processing using the Hugging Face Transformers library for text classification, named entity recognition, question answering, and text generation.
- Building Transformer Models with Attention Comprehensive guide to understanding and implementing transformer models with attention mechanisms using TensorFlow and Keras.
- Building Transformer Models from Scratch with PyTorch Guide to building transformer models from scratch using PyTorch, covering encoder-decoder architectures, positional encodings, and multi-head attention.
- Deep Learning for Natural Language Processing Covers deep learning methods for NLP including word embeddings, sequence-to-sequence models, and text classification using Keras.
- Deep Learning for Computer Vision Guide to computer vision using deep learning with Keras covering CNNs, object detection, and image classification.
- Deep Learning for Time Series Forecasting Covers MLPs, CNNs, and LSTMs for univariate, multivariate, and multi-step time series forecasting problems using Python.
- Generative Adversarial Networks with Python Guide to building GAN models using Python/Keras including DCGANs, Pix2Pix, and CycleGAN for image-to-image translation.
- Long Short-Term Memory Networks with Python Deep dive into LSTM networks for sequence prediction covering data preparation, architectures, parameter tuning, and model updates using Keras.
- XGBoost with Python Guide to gradient boosted decision trees using the XGBoost library covering model development, evaluation, and hyperparameter tuning.
- Time Series Forecasting with Python Covers classical and machine learning methods for time series forecasting including ARIMA and deep learning approaches.
- Ensemble Learning Algorithms with Python Covers bagging, boosting, stacking, and voting ensemble methods for improving predictive model performance.
- Better Deep Learning Techniques for improving deep learning model performance including regularization, learning rate scheduling, and ensemble methods.
- Statistical Methods for Machine Learning Foundation book on statistical methods required for understanding machine learning algorithms and their behavior.
- Linear Algebra for Machine Learning Foundation book on linear algebra covering matrices, vectors, matrix decomposition, and their applications in ML.
- Probability for Machine Learning Foundation book on probability theory covering distributions, Bayes theorem, and uncertainty quantification for ML.
- Optimization for Machine Learning Covers optimization algorithms used in training ML models including gradient descent, stochastic methods, and constrained optimization.
- Calculus for Machine Learning Foundation book on calculus covering derivatives, partial derivatives, gradients, and their role in neural network training.
- Python for Machine Learning Python programming guide covering language features, debugging tools, and ecosystem tools relevant to ML development.
- Machine Learning Mastery with Weka No-code machine learning guide using the Weka workbench for beginners without programming experience.
- Machine Learning Mastery with R Guide to machine learning using R with the caret package covering algorithms and predictive modeling.
- Data Preparation for Machine Learning Covers data cleaning, feature selection, feature engineering, and dimensionality reduction techniques for ML projects.
- Imbalanced Classification with Python Covers techniques for handling imbalanced datasets including SMOTE, cost-sensitive learning, and specialized performance metrics.
- Machine Learning in OpenCV Guide to using OpenCV's machine learning module for image processing and computer vision tasks.
- Machine Learning Algorithms From Scratch Step-by-step tutorials on implementing machine learning algorithms from scratch in Python for learning purposes.
- The Beginner's Guide to Data Science Introduction to data science covering foundational concepts, tools, and practical techniques for beginners.
- Next-Level Data Science Advanced data science techniques covering regression models, tree-based models, and statistical analysis with Python.
- Maximizing Productivity with ChatGPT Guide to using ChatGPT and AI assistants for improving productivity in data science and ML workflows.
- Mastering Digital Art with Stable Diffusion Guide to using Stable Diffusion for AI-generated digital art and image creation.
Quantifiable outcome
- Data Scientist salaries begin at $100,000 to $150,000; Machine Learning Engineer salaries are even higher
Companies that use Machine Learning Mastery
Customer profileNamed customers4 records
Segments4 records
Ideal customer profiles4 records
Machine Learning Mastery technology and API
TechnologyTechnology focussed No
API detail
- Has API
- No
- API docs
- API detail
Core technology
AI maturity
App detail
AI capability13 records
Feature3 records
Machine Learning Mastery partnerships and signals
Strategic signalPartnerships
One partnership is on record.
- Guiding Tech MediacoreMachine Learning Mastery is part of Guiding Tech Media, a leading digital media publisher focused on helping people figure out technology. Parent company provides corporate infrastructure and brand backing for the ML education business.
Scale indicators2 records
Recent moves5 records
Expansion highlights5 records
Machine Learning Mastery competitors and assessment
Company assessmentDirect peers
- Pragmatic Bookshelf: Independent publisher of technical books for working developers, sold directly via own storefront in ebook and print formats. Closely aligned on direct-to-developer sales model and practitioner-focused content.
- Apress: Technical book publisher with deep catalog in programming, data science, and AI/ML, selling to professionals and academics. Comparable on book publishing format and ML/AI topic coverage.
- Manning Publications: Independent publisher of technical books (often in early-access MEAP format) targeting software developers, including titles on machine learning, deep learning, and data science. Highly comparable business model: digital-first technical book publisher selling to individual developers via own storefront.
- Fast.ai: Provider of free, practical deep learning courses and the fastai library, with a strong top-down practitioner teaching style. Highly comparable teaching philosophy and developer target audience; differentiated only by being free and open source.
- No Starch Press: Independent technical publisher known for practical, developer-friendly books on programming, hacking, and data science. Similar independent-press DNA and developer audience overlap.
- DeepLearning.AI: Andrew Ng-founded AI education company offering courses, specializations, and the 'AI for Everyone' and deep learning series. Directly competes for the same developer/practitioner learner segment, though delivered as video courses rather than ebooks.
- Packt Publishing: Technical book publisher focused on software, data, and AI/ML topics, selling digital and print titles directly to developers. Closely matches MLM's model of curated technical book bundles and direct-to-developer e-commerce.
Broad incumbents
- Udacity: Online learning platform historically known for its AI and Machine Learning Nanodegree programs targeting developers and career changers. Comparable learner segments (developers entering ML, career switchers) though delivered as project-based video programs.
- Coursera: Large-scale online learning platform with extensive ML/AI specializations from top universities and companies. Overlaps on the applied ML learner market but at much broader scope and with subscription/credentialed course models.
- O'Reilly Media: Long-established technical book publisher and operator of the O'Reilly online learning platform with deep ML/AI catalog and a subscription-based offering. Significantly larger and broader, but overlaps directly on developer-focused ML book publishing and online learning content.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat4 records
Key risks7 records
Key highlights7 records
Customer concentration
Machine Learning Mastery social profiles
Digital presenceMachine Learning Mastery financial estimates
Financial estimateRevenue estimate
Valuation estimate
Machine Learning Mastery leadership team
Management profileNumber of profiles
Profiles4 records
Machine Learning Mastery funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Machine Learning Mastery M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Machine Learning Mastery
What does Machine Learning Mastery do?
Machine Learning Mastery operates an online educational content platform that sells PDF eBooks and free tutorials teaching applied machine learning to developers. The catalog includes 33+ eBooks covering foundational mathematics, classical ML algorithms, deep learning frameworks (Keras, PyTorch), and advanced topics like transformers, NLP, computer vision, GANs, and time series forecasting. Products are sold individually at $37 each or as themed bundles (e.g., the Super Bundle of 33 titles for $297) via the company's own e-commerce storefront with immediate digital download.
Is Machine Learning Mastery a public or private company?
Machine Learning Mastery is a private company. It is classified as founder individual operated bootstrapped and is currently operating.
When was Machine Learning Mastery founded?
Machine Learning Mastery was founded in 2013. It employs 1 to 10 people.
Where is Machine Learning Mastery based?
Machine Learning Mastery is headquartered in Melbourne, Australia, in the Oceania region.
How does Machine Learning Mastery make money?
Three revenue lines are on record. eBook Sales (Individual) is the primary driver. The others are eBook Bundles and discount Coupons.
Who are Machine Learning Mastery's main competitors?
Direct peers on record are Pragmatic Bookshelf, Apress, Manning Publications, Fast.ai, No Starch Press, DeepLearning.AI and Packt Publishing. Broad incumbents are Udacity, Coursera and O'Reilly Media.
Does Machine Learning Mastery have an API?
No public API is recorded for Machine Learning Mastery.
What industry is Machine Learning Mastery in?
Machine Learning Mastery's product category is Machine Learning Education. Its primary akta.pro industry code is BPAMAJAL, Online Learning Content & Course Publishing Platforms, with a secondary code of MPACALAA, Creator Course Marketplaces (Multi-Instructor Catalogs). Its NAICS code is 6114 and its SIC code is 2731.