Machine Learning Guide

MLG 001 Introduction


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Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

  • MLG, Resources Guide
  • Gnothi (podcast project): website, Github
What is this podcast?
  • "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
  • No math/programming experience required

Who is it for

  • Anyone curious about machine learning fundamentals
  • Aspiring machine learning developers

Why audio?

  • Supplementary content for commute/exercise/chores will help solidify your book/course-work

What it's not

  • News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
  • Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
  • iTunesU issues

Planned episodes

  • What is AI/ML: definition, comparison, history
  • Inspiration: automation, singularity, consciousness
  • ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
  • Math overview: linear algebra, statistics, calculus
  • Linear models: supervised (regression, classification); unsupervised
  • Parts: regularization, performance evaluation, dimensionality reduction, etc
  • Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
  • Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc
...more
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Machine Learning GuideBy OCDevel

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