Machine Learning Roadmap: A Curated Resource Guide for ML Beginners
A roadmap for getting started with Machine Learning
At a glance
- What is it?
- shanmukh05/Machine-Learning-Roadmap is a curated list of courses, books, and framework resources for engineers starting their machine learning journey, organized from math prerequisites through deep learning, assembled by an AI engineer at KLA Corporation.
- Who is it for?
- shanmukh05/Machine-Learning-Roadmap suits engineers who have identified that a guided reading list would help them start in machine learning and are unsure which courses or books to pick. It does not cover MLOps, deployment, or production monitoring, so practitioners who have already begun learning and need guidance on taking models to production will find the roadmap ends before the problems they face.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 19 days ago.
- What is it written in?
- GitHub does not report a main language for this repository.
Answers come from the project's GitHub data, last synced on October 1, 2026, and from our analysis. They are not legal advice.
Editorial analysis
Why This Roadmap Exists and Who Built It
The author, Shanmukha Sainath, is an AI Engineer at KLA Corporation and a graduate of the Department of Electronics and Electrical Communication Engineering at IIT Kharagpur. The README states the motivation directly: the internet offers so many resources for learning machine learning that choosing among them is itself a barrier. Having experienced that confusion personally, the author collected what they considered the best resources and published them as a structured list.
The intended audience is someone who wants to enter machine learning and needs a curated starting point rather than spending time evaluating dozens of competing courses and textbooks. The roadmap is explicitly opinionated: it names specific courses, specific books, and specific frameworks rather than cataloguing every available option. That focus is its main value and its main limitation.
The repository is hosted at shanmukh05.github.io/Machine-Learning-Roadmap/ as a GitHub Pages site, which gives the content a persistent URL beyond the raw GitHub README. The repository itself contains a README.md, a _config.yml for Jekyll, LICENSE, and a linktree.png.
The Prerequisite Stack: Mathematics and Programming
Before reaching machine learning content, the roadmap lays out four mathematical prerequisites and one programming prerequisite.
For mathematics, the roadmap lists Linear Algebra, Matrix Algebra, Probability and Statistics, and Calculus. For each, it names a specific course. Linear Algebra points to MIT OpenCourseWare's 18.06 course. Matrix Algebra and Statistics and Probability point to Khan Academy courses. Differential Calculus points to another Khan Academy course.
For programming, the roadmap lists Data Structures and Algorithms, with MIT OpenCourseWare's 6.006 Introduction to Algorithms course as the recommendation, and Python fundamentals, pointing to the W3Schools Python tutorial.
Noting these prerequisites matters because many ML learning plans skip them or treat them as optional. The roadmap's positioning of math and algorithms before any ML content signals that it is aimed at learners who are willing to build foundations rather than jump straight to applying pre-built models. Engineers who already have these foundations can skip the prerequisites section and move directly to the ML and deep learning sections.
Core Machine Learning: Courses and Textbooks
The ML section lists two courses and three textbooks. The courses are Andrew Ng's Machine Learning Specialization on Coursera, described as the new version, and Machine Learning A-Z on Udemy. The README does not give instructions on how to access them beyond the links; both are paid platforms that may offer free audit options.
The three textbooks listed are Pattern Recognition and Machine Learning by Christopher Bishop, An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani, and Hands on Machine Learning with Scikit-Learn and TensorFlow by Aurelien Géron. The README links to PDF versions of the first two and to an Amazon listing for the Géron book.
This is a focused selection. The Bishop and James et al. books represent the theoretical end of the spectrum, while the Géron book is more hands-on and code-oriented. The roadmap does not say which of these to start with or how to pace through them alongside the courses. That sequencing is left to the learner.
Deep Learning Progression: From Foundations to Production
The deep learning section adds four courses and five books. The courses are Andrew Ng's Deep Learning Specialization on Coursera, the PyTorch for Deep Learning Professional Certificate on DeepLearning.AI, Deep Learning with PyTorch by Yann LeCun through NYU on YouTube, and Deep Learning with fast.ai by Jeremy Howard on the fast.ai platform.
The books listed are Deep Learning by Goodfellow, Bengio, and Courville; Deep Learning with Python by François Chollet; two editions of Géron's Hands On Machine Learning book (one covering Scikit-Learn, Keras, and TensorFlow, another covering Scikit-Learn and PyTorch); and Dive into Deep Learning by Amazon scientists, available at d2l.ai.
The Yann LeCun and Jeremy Howard courses represent a practical, research-adjacent approach distinct from the more systematic Andrew Ng courses. Having both in the same list reflects that different learners benefit from different teaching styles. The Dive into Deep Learning book is notable for being free online and for including runnable code examples.
The README was truncated before the Frameworks section, so the frameworks and libraries coverage is not available for review.
How to Use This Repository
The repository is a single Markdown file rendered as a GitHub Pages site. There is no software to install, no CLI, and no generated output. The workflow is: clone or bookmark the repository, work through the sections in order, and follow the external links to the courses and textbooks as you go.
The structure uses a numbered table of contents with internal anchor links: Introduction, Prerequisites, Machine Learning, Deep Learning, Frameworks and Libraries, What's Next, and Other Resources. Navigating through the GitHub Pages site is more readable than the raw Markdown because the nested HTML list structure renders clearly in a browser.
Because the repository contains only static content, keeping it current requires the author to manually update links when courses change URLs or get removed. Some of the course links point to specific Coursera and Udemy URLs that may become stale as platform offerings change.
What the Roadmap Omits and Its Practical Limits
The roadmap covers the learning phase from first principles to deep learning frameworks. It does not document MLOps, model deployment, serving infrastructure, monitoring, or production concerns. Engineers who need guidance on moving a trained model into production will find the roadmap stops well before their problem.
The roadmap also does not include project-based learning. The resources listed are courses and textbooks, not project templates or dataset-driven exercises. A learner following this roadmap will finish it having read and watched a substantial amount, but without directed experience building and shipping a system.
The README notes that feedback and suggestions are welcome, which signals that the roadmap is a living document. However, with only one author and no indication of a review process, there is no mechanism to verify that all recommended courses still exist or that newer, better options have been added as the field evolves. Roadmaps maintained by communities or organizations typically update more reliably than individually maintained ones.
An Alternative Approach: roadmap.sh and Community-Maintained Guides
roadmap.sh publishes developer roadmaps as interactive visual directed graphs viewable in a browser. It includes an AI and Data Science track. The key difference from shanmukh05/Machine-Learning-Roadmap is structural: roadmap.sh organizes concepts as a graph with explicit dependencies and multiple valid paths, rather than a flat ordered list. It is maintained by a larger contributor community, which means updates reflect broader consensus on what engineers currently need.
For a learner who wants to understand the conceptual dependencies between topics before choosing resources, roadmap.sh's visual format is more informative. For a learner who wants a pre-selected short list of specific courses to take, shanmukh05's roadmap is more prescriptive. The two serve different decision styles rather than the same one.
Editorial conclusion
shanmukh05/Machine-Learning-Roadmap suits engineers who have identified that a guided reading list would help them start in machine learning and are unsure which courses or books to pick. It does not cover MLOps, deployment, or production monitoring, so practitioners who have already begun learning and need guidance on taking models to production will find the roadmap ends before the problems they face. The repository is a personal curation from one engineer's experience, not a community-vetted curriculum. Verify that the Coursera courses listed are still available in the form described before building a learning plan around them.
Frequently asked questions
What is the Machine Learning Roadmap on GitHub?
The Machine Learning Roadmap by shanmukh05 is a curated list of courses, books, and resources for engineers starting in machine learning, organized from math prerequisites through deep learning and frameworks. It was assembled by an AI Engineer at KLA Corporation who is an IIT Kharagpur graduate.
How do I use the Machine Learning Roadmap to start learning ML?
The README recommends starting with the prerequisites (Linear Algebra, Matrix Algebra, Probability and Statistics, Calculus, and Python) before moving to the Machine Learning and Deep Learning sections. Each topic links to a specific recommended course or book. There is no software to install; work through the linked resources in the listed order.
What resources does the Machine Learning Roadmap include?
The roadmap lists MIT and Khan Academy math courses for prerequisites, Andrew Ng's Machine Learning Specialization on Coursera and Machine Learning A-Z on Udemy for ML, and Andrew Ng's Deep Learning Specialization, fast.ai, and Yann LeCun's NYU course for deep learning. Key books include works by Bishop, Géron, Goodfellow, and the free Dive into Deep Learning at d2l.ai.
Official sources
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