Eikosstatistics & data science

Machine Learning & Artificial Intelligence · Big Data & Distributed Computing

Big Data: Predictive Modeling and Machine Learning at Scale

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction // DISTRIBUTED COMPUTATION & ITERATIVE ALGORITHMS

Overview

Topics covered

  • Introduction
  • Introduction to Machine Learning at Scale
  • Amdahl's Law versus Parallel Computing
  • The MapReduce Paradigm and the Hadoop Ecosystem
  • MapReduce Statistical Limitations
  • Apache Spark: Internal Architecture and Distributed Execution

Related concepts

Main connections

  • Linear algebra & matrices
  • Preprocessing pipelines & transform recipes
  • Regularization (Lasso / Ridge / Elastic Net)
  • Directed acyclic graphs (DAGs)
  • SQL & relational data modeling
  • Class imbalance & resampling

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

Slatencia(s)=1(1−p)+ps
View LaTeX sourceS_{\text{latencia}}(s) = \frac{1}{(1 - p) + \frac{p}{s}}