question_answer ANECDOTE Speaker's Career Transition ios_share
Speaker 0 transitioned from web/mobile development to machine learning.
He emphasizes the possibility of self-learning ML with a CS background.
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volunteer_activism ADVICE Self-Teaching Machine Learning ios_share
Supplement MLG with rigorous self-teaching.
Use the structured resource guide on ocdevel.com/mlg.
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volunteer_activism ADVICE MLG Resources ios_share
Visit ocdevel.com/mlg for a curated list of resources.
Filter by format, price, and quality to manage your learning.
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Get the app 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.
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 
