Machine Learning
Machine Learning (ML) is a subfield of AI in which systems improve their performance on a task by learning patterns from data, rather than following explicitly programmed rules. An ML model is trained on examples, adjusts internal parameters to minimize prediction error, and then generalizes that learned pattern to…
209 resources across 5 libraries
Glossary Terms(12)
Artificial Intelligence
Artificial Intelligence (AI) is the field of computer science concerned with building systems that perform tasks normally requiring human intelligence, such as…
Machine Learning
Machine Learning (ML) is a subfield of AI in which systems improve their performance on a task by learning patterns from data, rather than following explicitly…
Deep Learning
Deep Learning is a subset of machine learning that uses artificial neural networks with many layers ('deep' architectures) to automatically learn hierarchical…
Neural Network
A neural network is a computational model composed of interconnected layers of simple processing units called neurons, loosely inspired by biological brains. E…
Embeddings
Embeddings are dense numerical vector representations of data — such as words, sentences, images, or documents — learned so that semantically similar items are…
Inference
Inference is the process of running a trained machine learning model on new input data to produce a prediction, classification, or generated output. It is dist…
Fine Tuning
Fine-tuning is the process of taking a pretrained machine learning model and continuing its training on a smaller, task- or domain-specific dataset to adapt it…
AI Ethics
AI ethics is the interdisciplinary field concerned with identifying and addressing the moral and societal implications of designing, deploying, and using AI sy…
Comet ML
Comet ML is an MLOps platform for tracking machine learning experiments, managing model versions, and monitoring models in production, similar in purpose to We…
Anyscale
Anyscale is a managed cloud platform, built by the creators of the open-source Ray framework, for running and scaling distributed Python and machine learning w…
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables from a dataset to use in building a machine learning model, discardi…
Principal Component Analysis
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated compo…
Study Notes(45)
Clustering Keys
Understand how clustering keys influence micro-partition organization in Snowflake to improve pruning and query performance on large tables.
R and Machine Learning
Explore how to train, evaluate, and tune machine learning models in R using caret, tidymodels, and core algorithms like random forests and logistic regression.
Julia and Machine Learning (Flux.jl)
How Flux.jl brings differentiable programming to Julia, letting you build and train neural networks with plain Julia code and automatic differentiation.
RabbitMQ Clustering Basics
Learn how RabbitMQ nodes join together to form a cluster, how metadata and queue state are shared, and what happens when nodes fail.
Activation Functions
Activation functions inject non-linearity into neural networks, letting them approximate complex functions instead of collapsing into a single linear transform…
Anomaly Detection Basics
An overview of how machine learning identifies rare, unusual data points using statistical, distance-based, and model-based techniques across supervised and un…
Bias-Variance Tradeoff
A foundational concept explaining how model error decomposes into bias, variance, and irreducible noise, and how balancing them guides model complexity choices.
Common Machine Learning Pitfalls
A tour of the mistakes that most often derail machine learning projects, from data leakage to misleading metrics, and how to catch them before they cost you.
Confusion Matrix and Classification Metrics
A structured breakdown of correct and incorrect predictions by class, forming the foundation for accuracy, precision, recall, and other classification metrics.
Cross-Validation Strategies
Learn how k-fold, stratified, and time-series cross-validation give more reliable estimates of model performance than a single train-test split.
Data Cleaning Basics
Covers the core techniques for detecting and fixing messy real-world data — duplicates, inconsistent formatting, outliers, and type errors — before it reaches…
Datasets, Features, and Labels
Explains the vocabulary and structure of ML data — datasets, samples, features, and labels — and how they are organized before a model can be trained.
DBSCAN and Density-Based Clustering
Learn how DBSCAN groups points by local density rather than distance to a centroid, letting it discover arbitrarily shaped clusters and flag outliers automatic…
Decision Trees
Covers how decision trees split data recursively using impurity measures like Gini and entropy to produce interpretable, rule-based predictions.
Dimensionality Reduction and PCA
See how Principal Component Analysis compresses many correlated features into fewer uncorrelated components while preserving most of the variance in data.
Encoding Categorical Variables
Learn how to convert non-numeric categories into numeric representations models can use, and when to choose one-hot, ordinal, or target encoding.
Evaluating Regression Models
Surveys the core metrics for assessing regression quality — MAE, MSE, RMSE, and R-squared — and explains when each is the right lens for model performance.
Feature Scaling and Normalization
Learn why rescaling numeric features onto comparable ranges matters, and how standardization, min-max scaling, and robust scaling each affect model behavior.
Feature Selection Techniques
Discover filter, wrapper, and embedded methods for choosing the most useful subset of features, reducing overfitting and improving model interpretability.
Gradient Descent Explained
Understand how gradient descent iteratively adjusts model parameters to minimize a loss function, and how learning rate and variants like SGD affect convergenc…
Handling Missing Data
Explores why data goes missing, the different missingness mechanisms, and practical strategies — from deletion to imputation — for preparing incomplete dataset…
Hierarchical Clustering
An unsupervised technique that builds a nested tree of clusters (a dendrogram) by successively merging or splitting groups, avoiding the need to pre-specify a…
Hyperparameter Tuning
Explore systematic strategies — grid search, random search, and cross-validation-based tuning — for choosing model settings that are not learned directly from…
Intro to scikit-learn and the ML Toolchain
Scikit-learn provides a consistent fit/predict interface for building, evaluating, and chaining machine learning models, and anchors the wider Python ML toolch…
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Blog Articles(35)
Build a Cricket Win Predictor and Learn Machine Learning
A comprehensive guide to build a cricket win predictor and learn machine learning — written for learners at every level.
Top 10 AI Tools You Must Know in 2026
The best AI tool depends on the job — this roundup covers ten must-know tools for chat, coding, design, and productivity.
What Is Artificial Intelligence? A Beginner's Guide
A comprehensive guide to what is artificial intelligence? a beginner's guide — written for learners at every level.
Machine Learning vs Deep Learning vs AI Explained
A comprehensive guide to machine learning vs deep learning vs ai explained — written for learners at every level.
How ChatGPT Works: Explained Simply
A comprehensive guide to how chatgpt works: explained simply — written for learners at every level.
Prompt Engineering for Beginners: A Practical Guide
A comprehensive guide to prompt engineering for beginners: a practical guide — written for learners at every level.
Claude vs ChatGPT vs Gemini: Which Is Best?
A comprehensive guide to claude vs chatgpt vs gemini: which is best? — written for learners at every level.
Large Language Models (LLMs) Explained for Beginners
An LLM predicts the next piece of text, one token at a time — this guide explains how ChatGPT, Claude, and Gemini actually work.
Generative AI Explained: From Text to Images
Generative AI creates new content from patterns it learned — understand how text generation, image synthesis, and more work.
AI Agents Explained: The Next Big Thing
An AI agent acts to achieve a goal, not just answers a question — learn how agentic AI works and why it matters.
Neural Networks Explained with Simple Analogies
Neural networks are webs of simple units trained to recognise patterns — explained here without any maths.
20 ChatGPT Prompts to Boost Your Productivity
Great prompts share four parts: role, task, context, and format — here are 20 ready-to-use prompts for daily work.
Best AI Tools for Students in 2026
Used wisely, AI tools help you understand faster and study smarter — here are the best options for students in 2026.
Best AI Tools for Developers in 2026
GitHub Copilot, Cursor, and more — the standout AI tools that developers are using daily in 2026.
AI vs Human Jobs: What's Really at Risk?
AI mostly automates tasks, not whole jobs — an honest look at which roles are most exposed and which are safe.
How AI Recommendation Systems Work
Streaming apps know what you'll like because of content-based and collaborative filtering — here's how.
What Is Computer Vision? Real-World Examples
Computer vision lets machines interpret images — from medical scans to self-driving cars, explained simply.
Natural Language Processing (NLP) for Beginners
NLP is AI for human language — learn how machines read, understand, and generate text.
RAG Explained: How AI Answers From Your Data
RAG lets AI answer from your private documents instead of just its training data — here's how it works.
AI Ethics: Bias, Fairness and Responsibility
As AI makes more decisions affecting people, fairness, transparency, and accountability become essential.
How to Become an AI Engineer (Roadmap 2026)
A clear, step-by-step roadmap from Python foundations to deploying AI systems in production.
HTML and CSS for Beginners: Build Your First Web Page
HTML gives a page its structure; CSS gives it style — build your first real web page from scratch.
AI Agents Explained: How They Actually Work
AI agents are transforming what software can do autonomously — from booking travel to writing and running code. This guide explains the agent loop, tool use, m…
Prompt Engineering: Get Better Results from Any LLM
The difference between a mediocre AI output and an excellent one is usually the prompt. This guide covers the techniques that consistently produce better resul…
Showing 24 of 35.
Cheat Sheets(111)
Pandas Cheat Sheet
Pandas data manipulation, filtering, grouping, and analysis techniques.
NumPy Cheat Sheet
NumPy arrays, operations, broadcasting, and linear algebra essentials.
TensorFlow Cheat Sheet
TensorFlow 2 model building, training loops, and deployment basics.
Scikit-learn Cheat Sheet
Core scikit-learn workflow covering train/test splitting, pipelines, preprocessing, common estimators, cross-validation, hyperparameter tuning, and evaluation…
PyTorch Cheat Sheet
Essential PyTorch syntax for tensors, autograd, building neural network modules, and writing a standard training loop for deep learning models.
Keras Cheat Sheet
High-level Keras API reference covering Sequential and Functional model building, compiling, training with callbacks, and common layer types.
Matplotlib Cheat Sheet
Matplotlib plotting reference covering the object-oriented API, subplots, common chart types, and styling options for publication-ready figures.
Seaborn Cheat Sheet
Seaborn statistical visualization cheat sheet covering distribution, relational, and categorical plots plus heatmaps and figure-level facet grids.
Plotly Cheat Sheet
Plotly reference for building interactive charts with the Express and Graph Objects APIs, subplots, and exporting to HTML or images.
SciPy Cheat Sheet
SciPy scientific computing reference covering statistics, optimization, linear algebra, and interpolation for numerical Python workflows.
Statsmodels Cheat Sheet
Statsmodels reference for OLS and logistic regression, the R-style formula API, and ARIMA time-series modeling with full statistical summaries.
XGBoost Cheat Sheet
XGBoost cheat sheet covering the scikit-learn and native training APIs, key hyperparameters, early stopping, and feature importance.
LightGBM Cheat Sheet
LightGBM reference covering the scikit-learn and native Dataset APIs, leaf-wise tree growth, and the hyperparameters that control speed and overfitting.
CatBoost Cheat Sheet
CatBoost reference covering native categorical feature handling, the Pool data structure, ordered boosting, and built-in cross-validation.
OpenCV Cheat Sheet
OpenCV computer vision reference covering image I/O, filtering, edge detection, contours, drawing, and video capture in Python.
NLTK Cheat Sheet
NLTK natural language toolkit reference covering tokenization, stopword removal, stemming, lemmatization, part-of-speech tagging, and named entity chunking.
spaCy Cheat Sheet
spaCy reference covering pretrained pipelines, tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and similarity scoring.
Hugging Face Transformers Cheat Sheet
Hugging Face Transformers reference covering the pipeline API, AutoTokenizer/AutoModel classes, and fine-tuning with the Trainer API.
Apache Spark (PySpark) Cheat Sheet
PySpark reference covering SparkSession setup, DataFrame transformations and actions, Spark SQL queries, caching, and writing partitioned output.
Dask Cheat Sheet
Dask reference covering parallel DataFrame and Array APIs, delayed task graphs, lazy evaluation, and the distributed scheduler client.
Jupyter Notebook Cheat Sheet
Jupyter Notebook reference covering magic commands, shell integration, cell types, and essential keyboard shortcuts for efficient interactive computing.
Google Colab Cheat Sheet
A quick reference for running Python notebooks in Google Colab, covering GPU/TPU setup, mounting Drive, installing packages, and useful keyboard shortcuts.
MLflow Cheat Sheet
A reference for MLflow's experiment tracking, model registry, and CLI commands used to log, compare, and deploy machine learning models.
Weights & Biases Cheat Sheet
A guide to Weights & Biases for experiment tracking, hyperparameter sweeps, artifact versioning, and logging metrics and media during model training.
Showing 24 of 111.
Interview Questions(6)
How Would You Represent a Sparse Matrix Efficiently?
A sparse matrix — one where the vast majority of entries are zero — should be stored by recording only the non-zero values and their coordinates, using structu…
How to Design a Recommendation Engine
A recommendation engine splits work into an offline pipeline that trains models and precomputes candidate item lists from historical interaction data, and an o…
How to Design a Fraud Detection System
A fraud detection system scores every transaction in real time by combining a low-latency rules engine for known bad patterns with a machine-learning model for…
How to Design a Content Moderation System
A content moderation system pairs automated classifiers that screen every upload in real time with a tiered human review queue for borderline cases, so clearly…
How to Design a News Feed Ranking System
A news feed ranking system fetches a large candidate set of recent posts from people and pages a user follows, scores each candidate with a machine-learned mod…
How to Design an Ad Serving System?
An ad serving system is designed around an ultra-low-latency ad-selection path (typically under 100ms) that ranks pre-indexed, targeted candidate ads from an i…