Blogs worth reading
A curated list of the best AI, ML, and Data Science writing from Medium, personal blogs, and big-tech engineering teams. Stop scrolling Twitter — start reading.
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Original long-form guides from the team, tightly tied to the tools on this site.
Linear Regression for Machine Learning: The Complete Beginner's Guide (with Python)
Linear regression is the 'hello world' of machine learning — and understanding it properly teaches you the ideas (loss, gradient descent, bias-variance, regularisation) b…
Read →Logistic Regression Explained: From Sigmoid to a Working Classifier (with Python)
Despite the name, logistic regression answers yes/no questions — will this email be spam, will this customer churn? It is the most important classification baseline in ma…
Read →Decision Trees Explained: How Machines Learn to Ask the Right Questions (with Python)
A decision tree learns a flowchart of yes/no questions that split your data into ever-purer groups. It is the most human-readable model in machine learning — and the buil…
Read →Random Forests Explained: Why a Crowd of Trees Beats One Expert (with Python)
A single decision tree overfits and is unstable. A random forest trains hundreds of trees on random slices of the data and averages their votes — turning a weak, wobbly m…
Read →K-Nearest Neighbors (KNN) Explained: The Lazy Learner That Just Works (with Python)
K-Nearest Neighbors makes a prediction by asking its closest neighbours to vote. There is no training phase at all — it just memorises the data — which makes it the simpl…
Read →K-Means Clustering Explained: Finding Groups in Unlabelled Data (with Python)
K-Means is the go-to unsupervised algorithm for finding natural groups in data with no labels — customer segments, image colours, document topics. It teaches you clusteri…
Read →How to Crack the Data Analyst Interview in 2026: SQL, Stats, Case Studies & Behavioral Rounds
Most data analyst interviews follow the same five-stage shape — and the candidates who get offers aren't smarter, they've just rehearsed each stage deliberately. Here's e…
Read →Build an ML Portfolio That Gets You Hired: 7 Projects Recruiters Actually Respect in 2026
A Titanic notebook and an Iris classifier won't get you hired in 2026 — every applicant has them. These seven project archetypes prove you can build something real, and t…
Read →From Zero to LLM Engineer: A Practical 2026 Roadmap for the GenAI Job Market
'AI Engineer' is the fastest-growing role in tech, but the path is murky because it's new. This roadmap lays out exactly what to learn, in what order, and which projects …
Read →Cracking the ML System Design Interview: A Repeatable Framework with 3 Worked Examples
ML system design is the round that decides mid and senior offers — and the one candidates prepare for least. A repeatable framework turns an intimidating open prompt into…
Read →9 Data Science Resume Mistakes That Quietly Kill Your Applications (and How to Fix Them)
You rarely get told why your resume was rejected — it just goes quiet. These nine mistakes are the usual culprits for data science and ML applicants, and each has a fast,…
Read →How to Negotiate Your First Data / ML Job Offer in 2026 (Without Losing It)
Most first-time candidates accept the first number out of fear — and leave real money on the table. Negotiation, done respectfully, almost never loses an offer, and here'…
Read →How to Pass the ATS in 2026: 11 Resume Tweaks That Actually Work for AI/ML/Data Roles
If your resume keeps getting silently rejected without a single human reading it, you're losing to the ATS — not the hiring manager. These eleven specific tweaks are the …
Read →Cold Email Templates That Got Me Referrals at FAANG (Without Being Cringe)
Cold outreach for referrals works — but the templates floating around LinkedIn are mostly generic, mass-produced, and get ignored. Here are the exact structures that cons…
Read →The Realistic 6-Month AI/ML Career Roadmap (Free Resources Only)
Six months is enough to go from "I know basic Python" to a real entry-level AI/ML/Data role — if you spend it on the right things and skip the bootcamp marketing trap. He…
Read →Machine Learning
Distill — Clear explanations of machine learning
Beautifully interactive deep-dives into ML concepts — attention, optimization, GNNs, RL. Best when you want to truly understand something visually.
Read →Towards Data Science — Machine Learning
The biggest ML publication on Medium. Practical tutorials, end-to-end projects, and career write-ups updated daily.
Read →Sebastian Raschka — Ahead of AI
Author of 'Build a Large Language Model (From Scratch)'. Deep, paper-grounded explainers on LLMs, fine-tuning, and ML research trends.
Read →Lilian Weng's Log
Long-form survey posts that have become canonical references — attention, prompt engineering, agents, reward hacking.
Read →Machine Learning Mastery
Hands-on tutorials with runnable code. Great for going from 'I read about it' to 'I built it' in scikit-learn, Keras, and PyTorch.
Read →Deep Learning & LLMs
The Illustrated Transformer
If you only read one post on transformers, read this one. Illustrated explanations of BERT, GPT, attention, and embeddings.
Read →Andrej Karpathy's blog
Classics like 'A Recipe for Training Neural Networks' and 'The Unreasonable Effectiveness of RNNs'. Few posts, all gold.
Read →Hugging Face Blog
Walkthroughs of new open models, fine-tuning recipes, RLHF, quantization, and inference tricks — from the people shipping them.
Read →Chip Huyen — Designing Machine Learning Systems
ML systems, production GenAI, RAG, agents, and the gap between research and reality. A must-read for ML/AI engineers.
Read →Anthropic Research
Frontier LLM research — interpretability, alignment, constitutional AI, agent capabilities. Heavier reading but the source of record.
Read →OpenAI Research
Papers and posts behind GPT, function calling, and reasoning models. Pair with the API documentation when shipping with them.
Read →Building a RAG System: 11 Strategies from Naive Retrieval to Production Accuracy
Walks from plain top-k retrieval up to contextual and late chunking, reranking, multi-query and agentic RAG, knowledge graphs and hierarchical retrieval — with code, and an honest note on the trade-off each one buys. Intermediate to advanced.
Read →Data Science & Analytics
Towards Data Science
The default Medium publication for data science. Filter by 'Editor's Picks' to avoid the noise.
Read →KDnuggets
One of the oldest data-science publications. Weekly digests, cheat sheets, and career advice for analysts and scientists.
Read →Stitch Fix Algorithms
Industrial DS done right — experimentation, causal inference, recommendations. Read for how DS actually shows up at a real company.
Read →Variance Explained
Tidy-data thinking, R + Python deep dives, and statistical reasoning by a former DataCamp / Heap chief data scientist.
Read →Eugene Yan
Practical essays on ML systems, recommender systems, applied LLMs, and how senior ICs work. Perennially shared on Twitter for a reason.
Read →MLOps & ML Engineering
Made With ML
A full free curriculum on going from notebook to production — pipelines, monitoring, CI/CD for ML, and testing.
Read →Neptune.ai Blog
Tool-agnostic write-ups on experiment tracking, model registries, drift detection, and choosing infra for ML teams.
Read →Weights & Biases — Fully Connected
Reports, interviews, and reproducible notebooks. Strong on training-pipeline best practices and evaluation.
Read →MLOps Community Blog
Practitioner voices on real MLOps problems — vector stores, feature stores, eval, and on-call for ML systems.
Read →Engineering Big Tech Blogs
Netflix Tech Blog
How Netflix builds personalization, A/B testing platforms, and ML infra at scale. Required reading for senior DS/ML candidates.
Read →Uber Engineering
Michelangelo (their ML platform), forecasting, geospatial ML, and large-scale data engineering.
Read →Airbnb Engineering & Data Science
Search ranking, pricing models, experimentation, and how Airbnb structures its DS function. Excellent for interview prep.
Read →Google Research Blog
The latest from Google's research arm — Gemini, robotics, health AI, responsible AI.
Read →Meta AI Blog
Llama releases, recommender systems, multimodal models, and infrastructure posts from FAIR.
Read →Spotify Engineering
Music recommendations, large-scale embeddings, audio ML, and the ML platform behind Discover Weekly.
Read →Statistics & Research
Andrew Gelman — Statistical Modeling
Bayesian thinking, causal inference, and merciless critique of bad statistics in published papers. Sharpens how you reason about data.
Read →Cross Validated — Most Voted
Top-voted Q&A on statistics and ML. The highest-voted answers are often a faster path to understanding than textbooks.
Read →The Gradient
Long-form essays surveying AI research areas — accessible enough to be readable, deep enough to be useful.
Read →AI Snake Oil
Princeton researchers cutting through AI hype. Read before believing the next viral benchmark claim.
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