2026-09-08
System Design
Lesson
Why One Cheap Shortcut Backwards Breaks the Whole Map
Subtitle: Smallest-first is a proof, not a habit.
machine-learning
2026-09-07
Security & Identity
Lesson
The Key Your Own Code Is Not Allowed to Read
Subtitle: A key can be usable without being readable.
machine-learning
2026-09-07
AI Algorithms Curriculum
Lesson
Teaching a Model What Good Looks Like When You Cannot Write It Down
You can write a loss function for "predict the next token" . You cannot write one for "helpful and not rude" . RLHF's move is to stop trying: instead of specifying the objective, you learn it from hum
machine-learning
2026-09-06
AI/ML
Lesson
You Pay for Training Once and for Answering Forever
1. SAME MODEL, DIFFERENT WORK — Training: forward pass, loss, backprop, gradients, weight update; stores batch activations and repeats across epochs. Inference: forward pass only; no gradients, histor
machine-learning
2026-09-06
Web & Browser APIs
Lesson
Blurring What Is Behind an Element Instead of the Element Itself
filter: blur() blurs the element you put it on — its own text, its own borders, its own children. That is almost never what you wanted, because you wanted the panel readable .
machine-learning
2026-09-05
System Design
Lesson
The Meeting Where Nobody Should Be Named
Subtitle: A post-mortem is a shape, not a mood.
machine-learning
2026-09-05
Cloud & Infrastructure
Lesson
The Backend You Did Not Write and Still Have to Run
Amplify is not one service, and there is no Amplify runtime hiding underneath . It is a code generator plus a deployment pipeline sitting over ordinary AWS parts.
machine-learning
2026-09-04
System Design
Lesson
The Structure That Only Ever Knows Its Smallest Item
Subtitle: One weak rule. One cheap answer.
machine-learning
2026-09-04
Engineering & Growth
Lesson
Crashing Early on Purpose So You Never Guess Later
Two halves, pulling in opposite directions. Fail fast says: stop dead the instant an assumption is violated. Fallback grace says: keep serving something rather than propagating a failure to the user.
machine-learning
2026-09-03
Web & Browser APIs
Lesson
The Collapsible Panel That Needs No JavaScript at All
The <details> element and its <summary> child form a native HTML disclosure widget. The browser handles the toggle, exposes an open boolean attribute you can read, set, and style against, fires a togg
machine-learning
2026-09-02
System Design
Lesson
The Function Call That Can Run Twice
Subtitle: It reads like local code. It is a network.
machine-learning
2026-09-02
Career & Learning Craft
Lesson
How to Lose an Argument and Still Ship on Monday
Disagree and commit is a decision rule with two halves, and it only works when both are present. First: your disagreement gets recorded before the decision — in the open, with the reasoning, and genui
machine-learning
2026-09-01
System Design
Lesson
What One More Nine Actually Costs You in Minutes
- Compute your nines from raw request logs, per region. Compare that with the dashboard headline.
machine-learning
2026-09-01
Career & Learning Craft
Lesson
The Document That Makes Disagreement Cheap
State the problem and its evidence first — quantities, not adjectives. Then the constraints you must respect. A proposal that opens with its solution is asking for agreement, not review.
machine-learning
2026-08-31
LLM & Inference
Lesson
Why the Bill Counts Two Kinds of Token
A completion token is a token the model generated . A prompt token is one it read . Providers bill them at different rates — typically 2× to 4× more for completion — and the asymmetry is not arbitrary
machine-learning
2026-08-31
System Design
Lesson
When Two Writes Must Stay in Order and the Rest May Not
Subtitle: Only linked writes are fixed in order. Left column - Causally related (order is fixed): - You read a value, then you write - Your write depends on that read - Every replica shows that order
machine-learning
2026-08-30
LLM & Inference
Lesson
The Wait Before the First Word Appears
Time to first token (TTFT) is the latency from sending a request to receiving the first streamed token. It is dominated by prefill : the single pass in which the model processes every prompt token in
machine-learning
2026-08-30
System Design
Lesson
A System That Is Always Up and Frequently Wrong
Subtitle: Uptime is not the same as being right. Left column - Availability - did it answer?: - Uptime: something came back - Counts responses, not truth - A stale cache scores high here - Redundancy
machine-learning
2026-08-29
System Design
Lesson
Cutting a Shape Out of a Rectangle the Browser Insists On
Subtitle: Clipping changes the picture, not the box. Left column - Layout: still a full rectangle: - Every element laid out is a box - It still occupies that rectangle - It pushes neighbours the same
machine-learning
2026-08-29
LLM & Inference
Lesson
Asking a Weaker Question First to Get a Better Answer
You have a hard, detail-heavy question. The instinct is to ask it directly. Step-back prompting says: don't . First ask a deliberately weaker, more general question — what principle governs this kind
machine-learning
2026-08-28
Cloud & Infrastructure
Lesson
The Copy That Does Not Happen Until You Write
Several processes share one instance of the data instead of each getting a private copy. The duplicate is created at the exact moment a process tries to modify it — not a moment sooner.
machine-learning
2026-08-28
System Design
Lesson
Ordering Events Without Trusting Any Clock
Subtitle: Throw away the clock. Keep a counter. Left column - Wall clock timestamps: - Two machines never agree exactly - Clocks drift apart every hour - A sync can jump time backwards - So a smaller
machine-learning
2026-08-27
System Design
Lesson
Write-Ahead Logging
Before a storage engine touches a data page, it appends a description of the change to a sequential log and forces that log to stable storage. The data pages themselves are updated later, lazily, in a
machine-learning
2026-08-27
System Design
Lesson
Choosing a Data Centre Before Choosing a Server
Subtitle: Two layers, two jobs, one request.
machine-learning
2026-08-27
System Design
Lesson
Bloom Filters
INSERT: for the key "apple", the three hash functions produce positions 2, 7 and 19. Set bits at positions {2, 7, 19} to 1. EXACTLY three cells of the 24 are filled -- cell 2, cell 7 and cell 19 -- an
machine-learning
2026-08-26
System Design
Lesson
The Outline That Appears for the Keyboard and Hides for the Mouse
Subtitle: Same ring - only the keyboard sees it.
machine-learning
2026-08-25
AI/ML
Lesson
Searching With Small Pieces and Answering With Big Ones
Subtitle: Index one sentence, return its neighbours.
machine-learning
2026-08-25
System Design
Lesson
Bloom Filters
A Bloom filter trades certainty for space . Instead of storing the items themselves, it stores a fingerprint of them in a fixed bit array — then answers one question only: is this item in the set?
machine-learning
2026-08-24
System Design
Lesson
The Copy That Does Not Happen Until You Write
Subtitle: Share one page until someone writes.
machine-learning
2026-08-24
System Design
Lesson
Circuit Breaker Pattern
When a downstream dependency gets slow or starts erroring, the naive caller keeps sending requests and keeps waiting on timeouts. Each waiting request holds a thread, a connection, a chunk of memory.
machine-learning
2026-08-23
System Design
Lesson
Why Shifting Right Is Not the Same as Dividing
Subtitle: They agree on positives. Not on signs.
machine-learning
2026-08-23
System Design
Lesson
CQRS — Command Query Responsibility Segregation
CQRS splits the single data model that normally serves both writes and reads into two independent models : a command model that changes state and returns nothing, and a query model that returns data a
machine-learning
2026-08-22
System Design
Lesson
One Endpoint That Returns Exactly the Fields You Asked For
Subtitle: One schema, many backends, one shape.
machine-learning
2026-08-22
AI Algorithms Curriculum
Lesson
Event Sourcing
A normal database stores the current answer and throws away the question. Event Sourcing inverts that: the only source of truth is an ordered, append-only log of facts that already happened — and curr
machine-learning
2026-08-21
System Design
Lesson
Why a Log Is a Better Queue Than a Queue
Subtitle: One takes it away. One remembers. Left column - Queue (delete on read): - Message consumed, then deleted - Second reader gets nothing - No rewind, no replay - Broker tracks work left to do -
machine-learning
2026-08-21
AI Algorithms Curriculum
Lesson
CAP Theorem
In a distributed data store, when the network between nodes drops messages — a partition — you cannot simultaneously keep every read consistent (all nodes agree on the latest write) and keep the syste
machine-learning
2026-08-20
AI/ML
Lesson
Learning From Data With No Labels
Subtitle: The labels come from the data.
machine-learning
2026-08-20
AI Algorithms Curriculum
Lesson
Consistent Hashing
The naive way to shard data is server = hash(key) mod N . It works perfectly until N changes. Go from 4 servers to 5 and almost every key's answer changes — roughly (N-1)/N of your data , about 80%, m
machine-learning
2026-08-19
Security & Identity
Lesson
Why One Ampersand Instead of Two Is a Real Bug
Use it when - Bitwise AND: permission masks, device flags
machine-learning
2026-08-19
AI Algorithms Curriculum
Lesson
Beam Search
Beam search is a bounded breadth-first walk over the tree of possible output sequences.
machine-learning
2026-08-18
System Design
Lesson
Why Your App Shows Stale Data Even Though the Write Succeeded
Subtitle: The write was fine. The read never promised to see it.
machine-learning
2026-08-17
AI Algorithms Curriculum
Lesson
Seq2Seq
Classifiers map an input to one label . Seq2Seq maps an input sequence of length T to an output sequence of length T′ — and T′ need not equal T. An encoder RNN/LSTM reads the whole input and squeezes
machine-learning
2026-08-17
Security & Identity
Lesson
Hashing vs Encryption vs Encoding
Use it when - ENCRYPTION: secrets in transit or at rest Use it when - ENCODING: binary in JSON, URLs, email
machine-learning
2026-08-16
AI Algorithms Curriculum
Lesson
Word2Vec
Word2Vec (Mikolov et al., 2013) takes that linguistic intuition and makes it a prediction task . Train a deliberately shallow network to predict a word from its context (or its context from the word),
machine-learning
2026-08-16
System Design
Lesson
Scaling: Vertical vs Horizontal vs Auto
Subtitle: Add power, add boxes, or add rules. Left column - VERTICAL (scale up): Bigger machine: more CPU and RAM No code change needed One box, one hard ceiling Resize usually means downtime Cost cli
machine-learning
2026-08-15
Career & Learning Craft
Lesson
Dual Coding: Words + Visuals
Subtitle: Your brain has two channels. Use both. Left column - Words only: One channel carries everything Text and talk share one path Feels fast, fills up fast Load hits a limit, detail drops Recall
machine-learning
2026-08-14
AI Algorithms Curriculum
Lesson
Monte Carlo Tree Search
Every node stores just two numbers: n (times visited) and w (total reward backed up through it). No board evaluation, no learned weights — two counters.
machine-learning
2026-08-14
Security & Identity
Lesson
CORS vs CSRF
Subtitle: One blocks reads. One forges writes. Left column - CORS: Browser rule for cross-site reads Server sends Allow-Origin header Blocks the page, not the request Guards your data from other sites
machine-learning
2026-08-13
AI Algorithms Curriculum
Lesson
Proximal Policy Optimization
Improve the policy as much as you can — but never trust one update too far.
Vanilla policy gradient has one fatal habit: a single large step can destroy a working policy, and there is no way back — the data that would correct it was collected by the policy you just wrecked. P
machine-learning
2026-08-13
System Design
Lesson
Monolith vs SOA vs Microservices vs Event-Driven
Subtitle: One axis explains all four - coupling.
machine-learning
2026-08-12
Career & Learning Craft
Lesson
Worked Examples vs Solving Cold
Subtitle: When to study the solution and when to struggle first Left column - WORKED EXAMPLES: See every step already solved Low mental load, room to see pattern Best when the topic is brand new Gives
machine-learning
2026-08-12
AI Algorithms Curriculum
Lesson
Actor-Critic Methods
Pure policy gradient (REINFORCE) is unbiased but wildly noisy — it waits for a whole episode's return before it learns anything, and that return swings hard. Pure value methods (Q-learning) are stable
machine-learning
2026-08-11
Security & Identity
Lesson
Passkeys vs Passwords: WebAuthn Basics
Subtitle: A shared secret vs a private key that never leaves your device
machine-learning
2026-08-11
AI Algorithms Curriculum
Lesson
CatBoost
CatBoost is gradient boosting on decision trees, engineered around one observation: the standard ways of handling categorical features leak the target into the features . Naive mean-target encoding co
machine-learning
2026-08-10
System Design
Lesson
The Consistency Ladder: Eventual to Strong
Subtitle: Five levels of read guarantee, weakest to strongest.
machine-learning
2026-08-10
AI Algorithms Curriculum
Lesson
LightGBM
LightGBM is gradient boosting where every expensive step has been replaced by a cheaper approximation that barely costs accuracy. Like all boosting, it fits trees sequentially, each one predicting the
machine-learning
2026-08-09
AI Algorithms Curriculum
Lesson
Spectral Clustering
k-Means draws straight lines . That is its whole geometry — every cluster is the set of points nearest one centroid, so every boundary is a flat hyperplane. Give it two interlocking crescent moons and
machine-learning
2026-08-09
Career & Learning Craft
Lesson
Elaborative Interrogation: Ask Why, Not What
Subtitle: Turn facts into reasons you can rebuild
machine-learning
2026-08-08
AI Algorithms Curriculum
Lesson
UMAP
UMAP assumes your high-dimensional data actually lives on a much lower-dimensional manifold — a curved sheet folded up inside a big space. It never trusts long distances. Instead it asks each point, "
machine-learning
2026-08-08
Security & Identity
Lesson
TOTP: How Authenticator Codes Work
Subtitle: A shared secret plus a shared clock, no network needed
machine-learning
2026-08-07
AI Algorithms Curriculum
Lesson
Expectation Maximization
Maximum likelihood is easy when you can see everything. EM is what you reach for when some of the data is missing by design — the cluster label, the regime, the hidden state was never recorded.
machine-learning
2026-08-07
System Design
Lesson
Back-of-the-Envelope Estimation
Subtitle: Get to a defensible number in five minutes
machine-learning
2026-08-06
Career & Learning Craft
Lesson
The Generation Effect: Guess Before You're Told
Subtitle: Struggle for the answer before you read it
machine-learning
2026-08-06
AI Algorithms Curriculum
Lesson
Hidden Markov Models
A Hidden Markov Model describes a system that moves through a sequence of hidden states you never observe directly. What you do observe are emissions — noisy signals produced by whichever state the sy
machine-learning
2026-08-05
Security & Identity
Lesson
Refresh Token Rotation
Subtitle: Short-lived access, single-use refresh, revoke the family on reuse
machine-learning
2026-08-05
AI Algorithms Curriculum
Lesson
Gaussian Processes
Encodes “how similar are two inputs?” and thereby every assumption about the function. RBF: σ²exp(−(x−x′)²/2ℓ²) . The lengthscale ℓ is the wiggle rate; small ℓ means twitchy, large ℓ means smooth. Ker
machine-learning
2026-08-04
System Design
Lesson
Observability: Logs vs Metrics vs Traces
Subtitle: Three signals, three questions, one system
machine-learning
2026-08-04
AI Algorithms Curriculum
Lesson
Bayesian Optimization
Grid search asks every question. Random search asks arbitrary questions. Bayesian Optimization asks the most informative next question — and typically finds a good optimum in tens of evaluations where
machine-learning
2026-08-03
Career & Learning Craft
Lesson
Feedback Loops: Tight vs Loose
Subtitle: How fast you learn is how fast you find out
machine-learning
2026-08-03
AI Algorithms Curriculum
Lesson
Federated Learning
machine-learning
2026-08-02
Security & Identity
Lesson
OAuth 2.0 Authorization Code Flow with PKCE
Subtitle: How a public app proves it started the login
machine-learning
2026-08-02
AI Algorithms Curriculum
Lesson
Contrastive Learning
Random crop, colour jitter, grayscale, blur, flip. This is the supervision signal — weak augmentation makes the task trivial, so the model learns nothing but colour histograms.
machine-learning
2026-08-01
AI Algorithms Curriculum
Lesson
Graph Neural Networks
A CNN assumes your data sits on a grid. An RNN assumes it sits on a line. A GNN drops both assumptions: it learns on arbitrary graphs , where each node has features and the edges say who is related to
machine-learning
2026-08-01
System Design
Lesson
Failure Handling: The Four Moves
Subtitle: Four moves that stop one slow service from sinking everything
machine-learning
2026-07-31
AI Algorithms Curriculum
Lesson
Diffusion Models
A diffusion model learns generation by learning destruction in reverse . Take a real image and add a little Gaussian noise, again and again, over T steps — eventually it is pure static. That forward p
machine-learning
2026-07-31
Career & Learning Craft
Lesson
Deep Work vs Shallow Work
Subtitle: Protect attention first, batch the rest
machine-learning
2026-07-30
Security & Identity
Lesson
Session Cookies vs JWT
Subtitle: Stateful memory vs stateless proof
machine-learning
2026-07-30
System Design
Lesson
PACELC: Latency vs Consistency
Subtitle: Latency and consistency are the everyday trade
machine-learning
2026-07-30
AI Algorithms Curriculum
Lesson
GPT Architecture
GPT throws away half of the original Transformer. No encoder, no cross-attention — just a tall stack of identical decoder blocks reading left to right. Every position may attend to itself and everythi
machine-learning
2026-07-29
Career & Learning Craft
Lesson
Retrieval Practice vs Re-reading
Subtitle: Pull it out of your head, do not push it in again
machine-learning
2026-07-29
AI Algorithms Curriculum
Lesson
BERT
BERT takes the Transformer and throws away the decoder. What remains is a deep encoder stack that reads an entire sentence in both directions at once — so the vector for a word is shaped by everything
machine-learning
2026-07-28
System Design
Lesson
Raft Leader Election
Subtitle: How a cluster agrees on exactly one leader
machine-learning
2026-07-28
AI Algorithms Curriculum
Lesson
Attention Mechanisms
Let every position look back at every other position — and weight what actually matters.
machine-learning
2026-07-27
Security & Identity
Lesson
Password Hashing: bcrypt vs Argon2id
Subtitle: Store passwords so stolen data stays useless
machine-learning
2026-07-27
Engineering & Growth
Lesson
Handle watermark verification
machine-learning
2026-07-27
Engineering & Growth
Lesson
Gradient descent intuition
machine-learning
2026-07-27
Engineering & Growth
Lesson
Generative Adversarial Networks (GANs)
Educational hand-drawn sketchnote explaining Generative Adversarial Networks as a two-player adversarial game. Center: a large loop diagram — random noise vector z (small dice/scribble cloud) feeding
machine-learning
2026-07-27
AI Algorithms Curriculum
Lesson
Generative Adversarial Networks
Most generative models ask you to write down a likelihood and maximise it. GANs refuse. Instead they train a Generator G that maps random noise z to samples, and a Discriminator D that scores samples
machine-learning
2026-07-26
AI Algorithms Curriculum
Lesson
Variational Autoencoders (VAE)
Learn a latent distribution instead of a single point, so the space between examples becomes generative.
machine-learninggenerativedeep-learningvae
2026-07-26
System Design
Lesson
Load Balancer vs API Gateway
Subtitle: Traffic distribution vs traffic control
machine-learning
2026-07-26
Career & Learning Craft
Lesson
Interleaving vs Blocked Practice
Subtitle: Mix your reps to build real judgment
machine-learning
2026-07-25
AI Algorithms Curriculum
Lesson
🧬 Genetic Algorithms
An encoded candidate solution — often a bitstring or vector of ‘genes’ representing the parameters being optimized.
machine-learning
2026-07-25
Security & Identity
Lesson
Mutual TLS (mTLS): Two-Way Trust
1) What mTLS Is: Standard TLS proves only the SERVER's identity to the client (one-way). mTLS adds the reverse: the CLIENT also presents a certificate, so BOTH sides prove who they are before any data
machine-learning
2026-07-24
AI Algorithms Curriculum
Lesson
Markov Decision Process (MDP)
machine-learning
2026-07-23
Career & Learning Craft
Lesson
The Feynman Technique
THE CORE IDEA: You do not understand something until you can explain it simply. The Feynman Technique turns passive review into active generation \u2014 you learn by teaching an imaginary novice, and
machine-learning
2026-07-23
AI Algorithms Curriculum
Lesson
Isolation Forest
machine-learning
2026-07-22
System Design
Lesson
Database Indexing
WHY INDEXES EXIST: A table scan reads every row (O(n)); an index is a sorted side-structure that turns lookups into O(log n). Trade-off: faster reads, slower writes, more storage — the librarian's car
machine-learning
2026-07-22
AI Algorithms Curriculum
Lesson
Autoencoders
Maps input x to a compressed code: f(x) = z. Layers shrink toward the bottleneck.
machine-learning
2026-07-21
AI Algorithms Curriculum
Lesson
k-Means++
Smart seeding that makes clustering start in the right place.
machine-learning
2026-07-21
System Design
Lesson
Rate Limiting Algorithms
Rate Limiting Algorithms — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-20
AI Algorithms Curriculum
Lesson
Transformer
The Transformer replaces recurrence with self-attention : every token looks at every other token in one parallel pass and decides how much to weigh each. No sequential loop means the whole sequence is
machine-learning
2026-07-19
AI Algorithms Curriculum
Lesson
Long Short-Term Memory
Plain RNNs multiply gradients through time, so error signal either vanishes or explodes over long sequences. The LSTM adds a protected cell state that information flows through with mostly additive up
machine-learning
2026-07-18
AI Algorithms Curriculum
Lesson
Recurrent Neural Network
An RNN is a neural network with a loop . At each step it reads one element of a sequence and blends it with a hidden state — a running memory of everything seen so far. That memory is what lets it mod
machine-learning
2026-07-17
Security & Identity
Lesson
JWT Security
JWT Security — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-17
AI Algorithms Curriculum
Lesson
Convolutional Neural Network
machine-learning
2026-07-16
AI Algorithms Curriculum
Lesson
Artificial Neural Network
The learnable parameters. Weights scale each input's influence; the bias shifts the activation threshold. Training = finding good values for these.
machine-learning
2026-07-15
AI Algorithms Curriculum
Lesson
Actor-Critic
Maps states to a distribution over actions. Updated in the direction the Critic says is advantageous.
machine-learning
2026-07-14
Career & Learning Craft
Lesson
Spaced Repetition
Spaced Repetition — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-14
AI Algorithms Curriculum
Lesson
Policy Gradient — REINFORCE
A network mapping state → action probabilities. Differentiable, so we can push gradients through it.
machine-learning
2026-07-13
AI Algorithms Curriculum
Lesson
Deep Q-Network (DQN)
machine-learning
2026-07-12
AI Algorithms Curriculum
Lesson
SARSA — On-Policy TD Control
(s, a, r, s', a') — the update needs the next action a' chosen by the current policy, not the max over actions.
machine-learning
2026-07-11
AI Algorithms Curriculum
Lesson
Q-Learning
The agent observes a state s , picks an action a , and gets reward r plus a next state s' . The (s,a,r,s') tuple is the unit of learning.
machine-learning
2026-07-10
AI Algorithms Curriculum
Lesson
t-SNE
High-D closeness modeled as a Gaussian probability — how likely point i picks j as a neighbor.
machine-learning
2026-07-10
System Design
Lesson
Caching Strategies
Caching Strategies — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-09
AI Algorithms Curriculum
Lesson
🎯 Principal Component Analysis
machine-learning
2026-07-08
AI Algorithms Curriculum
Lesson
DBSCAN
The neighborhood radius. How close two points must be to count as neighbors.
machine-learning
2026-07-07
AI Algorithms Curriculum
Lesson
Hierarchical Clustering
Hierarchical clustering builds a hierarchy of groups rather than a flat partition. The agglomerative (bottom-up) version starts with every point as its own cluster and repeatedly merges the two closes
machine-learning
2026-07-07
Career & Learning Craft
Lesson
Deliberate Practice
Deliberate Practice — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-06
AI Algorithms Curriculum
Lesson
k-Means Clustering
k-Means partitions data into k groups by minimizing the total within-cluster variance. Each point joins the cluster whose centroid (mean) is nearest, and centroids are re-computed until they stop movi
machine-learning
2026-07-05
AI Algorithms Curriculum
Lesson
XGBoost
machine-learning
2026-07-03
Security & Identity
Lesson
OAuth vs OIDC
OAuth vs OIDC — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-07-03
AI Algorithms Curriculum
Lesson
AdaBoost
Simple models (decision stumps) that only need to be slightly better than random guessing.
machine-learning
2026-07-02
AI Algorithms Curriculum
Lesson
Gradient Boosting
Build an ensemble sequentially, each weak tree correcting the residual errors of the trees before it.
machine-learningensembleboostinggradient-boosting
2026-07-01
AI Algorithms Curriculum
Lesson
Naive Bayes
Use Bayes' theorem plus a simplifying independence assumption to build a fast probabilistic classifier.
machine-learningclassificationbayesprobability
2026-06-30
AI Algorithms Curriculum
Lesson
k-Nearest Neighbors (k-NN)
Classify a point by looking at the labels of its nearest examples under a chosen distance metric.
machine-learningclassificationdistanceknn
2026-06-30
Algo & Data Structures
Lesson
Introduction to Algorithms
Understand the fundamental concept of algorithmic complexity and Big O notation for evaluating algorithm performance.
algorithmscomplexitybig-ofundamentals
2026-06-30
System Design
Lesson
Database Sharding
Database Sharding — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-06-29
AI Algorithms Curriculum
Lesson
Support Vector Machine (SVM)
Find the maximum-margin boundary that separates classes, with kernels for non-linear structure.
machine-learningclassificationsvmkernel
2026-06-28
AI Algorithms Curriculum
Lesson
Random Forest
Reduce variance by averaging many decorrelated decision trees trained on bootstrap samples.
machine-learningensemblebaggingrandom-forest
2026-06-27
AI Algorithms Curriculum
Lesson
Decision Tree
Make predictions through interpretable if-then splits chosen by information gain or impurity reduction.
machine-learningtreesentropygini
2026-06-26
AI Algorithms Curriculum
Lesson
Logistic Regression
Turn a linear score into a calibrated probability for binary classification.
machine-learningclassificationsigmoidlog-loss
2026-06-26
Security & Identity
Lesson
API Authentication
API Authentication — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
2026-06-25
AI Algorithms Curriculum
Lesson
Linear Regression
Predict continuous values by fitting the best linear relationship between features and a target.
machine-learningregressionleast-squaresgradient-descent
2026-06-25
Resource Digest
Resource Digest
10 Free AI Learning Platforms
Map high-signal free AI education platforms to the Sensei algorithm curriculum.
resourcesai-learningcurriculumdigest
2026-06-17
System Design
Lesson
Idempotency
Idempotency — engineering visual from the Sensei rich-content series. Tap the infographic to download the full-resolution version.
machine-learning
