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 Dept Agency 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 

Show notes: <a href="https://ocdevel.com/mlg/1" rel="noopener nofollow" target="_blank">ocdevel.com/mlg/1</a>. 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 <a href="http://ocdevel.com" rel="nofollow" target="_blank">ocdevel.com</a>. Audio is a great supplement during exercise, commute, chores, etc. <ul> <li><a href="https://ocdevel.com/mlg" rel="noopener nofollow" target="_blank">MLG</a>, <a href="https://ocdevel.com/mlg/resources" rel="noopener nofollow" target="_blank">Resources Guide</a></li> <li><a href="https://deptagency.com/" rel="noopener nofollow" target="_blank">Dept Agency</a></li> <li>Gnothi (podcast project): <a href="https://gnothiai.com/" rel="noopener nofollow" target="_blank">website</a>, <a href="https://github.com/lefnire/gnothi" rel="noopener nofollow" target="_blank">Github</a></li> </ul> What is this podcast? <ul> <li>"Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)</li> <li>No math/programming experience required</li> </ul> Who is it for <ul> <li>Anyone curious about machine learning fundamentals</li> <li>Aspiring machine learning developers</li> </ul> Why audio? <ul> <li>Supplementary content for commute/exercise/chores will help solidify your book/course-work</li> </ul> What it's not <ul> <li>News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines</li> <li>Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101</li> <li>iTunesU issues</li> </ul> Planned episodes <ul> <li>What is AI/ML: definition, comparison, history</li> <li>Inspiration: automation, singularity, consciousness</li> <li>ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications</li> <li>Math overview: linear algebra, statistics, calculus</li> <li>Linear models: supervised (regression, classification); unsupervised</li> <li>Parts: regularization, performance evaluation, dimensionality reduction, etc</li> <li>Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)</li> <li>Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc</li> </ul>

MLG 001 Introduction

Show notes at ocdevel.com/mlg/2 Updated! Skip to [00:29:36] for Data Science (new content) if you've already heard this episode. What is artificial intelligence, machine learning, and data science? What are their differences? AI history. Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI &amp; ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions. Artificial Intelligence (AI) - Wikipedia Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AlphaGo Movie, very good! Sub-disciplines  Reasoning, problem solving Knowledge representation Planning Learning Natural language processing Perception Motion and manipulation Social intelligence General intelligence  Applications  Autonomous vehicles (drones, self-driving cars) Medical diagnosis Creating art (such as poetry) Proving mathematical theorems Playing games (such as Chess or Go) Search engines Online assistants (such as Siri) Image recognition in photographs Spam filtering Prediction of judicial decisions Targeting online advertisements  Machine Learning (ML) - Wikipedia Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Data Science (DS) - Wikipedia Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. History  Greek mythology, Golums First attempt: Ramon Lull, 13th century Davinci's walking animals Descartes, Leibniz  1700s-1800s: Statistics &amp; Mathematical decision making  Thomas Bayes: reasoning about the probability of events George Boole: logical reasoning / binary algebra Gottlob Frege: Propositional logic   1832: Charles Babbage &amp; Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines  1936: Universal Turing Machine  Computing Machinery and Intelligence - explored AI!   1946: John von Neumann Universal Computing Machine 1943: Warren McCulloch &amp; Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-&gt; neural networks by way of MLP)  50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon  Newell &amp; Simon: Hueristics -&gt; Logic Theories, General Problem Solver Slefridge: Computer Vision NLP Stanford Research Institute: Shakey Feigenbaum: Expert systems GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems   70s: Lighthill report (James Lighthill), big promises -&gt; AI Winter  90s: Data, Computation, Practical Application -&gt; AI back (90s)  Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation   Bloomberg, 2015 was whopper for AI in industry AlphaGo &amp; DeepMind 

Show notes at <a href="https://ocdevel.com/mlg/2" rel="nofollow" target="_blank">ocdevel.com/mlg/2</a> Updated! Skip to [00:29:36] for Data Science (new content) if you've already heard this episode. What is artificial intelligence, machine learning, and data science? What are their differences? AI history. Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI &amp; ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions. Artificial Intelligence (AI) - <a href="https://en.wikipedia.org/wiki/Artificial_intelligence" rel="nofollow" target="_blank">Wikipedia</a> Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. <a href="https://www.alphagomovie.com/" rel="nofollow" target="_blank">AlphaGo Movie</a>, very good! <a href="https://en.wikipedia.org/wiki/Artificial_intelligence" rel="nofollow" target="_blank">Sub-disciplines</a> <ul> <li>Reasoning, problem solving</li> <li>Knowledge representation</li> <li>Planning</li> <li>Learning</li> <li>Natural language processing</li> <li>Perception</li> <li>Motion and manipulation</li> <li>Social intelligence</li> <li>General intelligence</li> </ul> Applications <ul> <li>Autonomous vehicles (drones, self-driving cars)</li> <li>Medical diagnosis</li> <li>Creating art (such as poetry)</li> <li>Proving mathematical theorems</li> <li>Playing games (such as Chess or Go)</li> <li>Search engines</li> <li>Online assistants (such as Siri)</li> <li>Image recognition in photographs</li> <li>Spam filtering</li> <li>Prediction of judicial decisions</li> <li>Targeting online advertisements</li> </ul> Machine Learning (ML) - <a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow" target="_blank">Wikipedia</a> Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Data Science (DS) - <a href="https://en.wikipedia.org/wiki/Data_science" rel="nofollow" target="_blank">Wikipedia</a> Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. History <ul> <li>Greek mythology, Golums</li> <li>First attempt: Ramon Lull, 13th century</li> <li>Davinci's walking animals</li> <li>Descartes, Leibniz</li> <li> 1700s-1800s: Statistics &amp; Mathematical decision making <ul> <li>Thomas Bayes: reasoning about the probability of events</li> <li>George Boole: logical reasoning / binary algebra</li> <li>Gottlob Frege: Propositional logic</li> </ul> </li> <li>1832: Charles Babbage &amp; Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines</li> <li> 1936: Universal Turing Machine <ul> <li>Computing Machinery and Intelligence - explored AI!</li> </ul> </li> <li>1946: John von Neumann Universal Computing Machine</li> <li>1943: Warren McCulloch &amp; Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-&gt; neural networks by way of MLP)</li> <li> 50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon <ul> <li>Newell &amp; Simon: Hueristics -&gt; Logic Theories, General Problem Solver</li> <li>Slefridge: Computer Vision</li> <li>NLP</li> <li>Stanford Research Institute: Shakey</li> <li>Feigenbaum: Expert systems</li> <li>GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems</li> </ul> </li> <li>70s: Lighthill report (James Lighthill), big promises -&gt; AI Winter</li> <li> 90s: Data, Computation, Practical Application -&gt; AI back (90s) <ul> <li>Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation</li> </ul> </li> <li>Bloomberg, 2015 was whopper for AI in industry</li> <li>AlphaGo &amp; DeepMind</li> </ul>

MLG 002 What is AI, ML, DS

Show notes at ocdevel.com/mlg/3. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.

Show notes at <a href="https://ocdevel.com/mlg/3" rel="nofollow" target="_blank">ocdevel.com/mlg/3</a>. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.

MLG 003 Inspiration

Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources

Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. <a href="http://ocdevel.com/mlg/4" rel="nofollow" target="_blank">ocdevel.com/mlg/4</a> for notes and resources

MLG 004 Algorithms - Intuition

Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources

Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression <a href="http://ocdevel.com/mlg/5" rel="nofollow" target="_blank">ocdevel.com/mlg/5</a> for notes and resources

MLG 005 Linear Regression

Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources

Discussion on certificates and degrees from Udacity to a Masters degree. <a href="http://ocdevel.com/mlg/6" rel="nofollow" target="_blank">ocdevel.com/mlg/6</a> for notes and resources

MLG 006 Certificates & Degrees

Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources

Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! <a href="http://ocdevel.com/mlg/7" rel="nofollow" target="_blank">ocdevel.com/mlg/7</a> for notes and resources

MLG 007 Logistic Regression

Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ocdevel.com/mlg/8 for notes and resources

Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. <a href="http://ocdevel.com/mlg/8" rel="nofollow" target="_blank">ocdevel.com/mlg/8</a> for notes and resources

MLG 008 Math

Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. ocdevel.com/mlg/9 for notes and resources

Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. <a href="http://ocdevel.com/mlg/9" rel="nofollow" target="_blank">ocdevel.com/mlg/9</a> for notes and resources

MLG 009 Deep Learning

Languages &amp; frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. ocdevel.com/mlg/10 for notes and resources

Languages &amp; frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. <a href="http://ocdevel.com/mlg/10" rel="nofollow" target="_blank">ocdevel.com/mlg/10</a> for notes and resources

MLG 010 Languages & Frameworks

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), 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.

Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ocdevel.com/mlg/12 for notes and resources

Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees <a href="http://ocdevel.com/mlg/12" rel="nofollow" target="_blank">ocdevel.com/mlg/12</a> for notes and resources

MLG 012 Shallow Algos 1

Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ocdevel.com/mlg/13 for notes and resources

Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. <a href="http://ocdevel.com/mlg/13" rel="nofollow" target="_blank">ocdevel.com/mlg/13</a> for notes and resources

MLG 013 Shallow Algos 2

Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC). ocdevel.com/mlg/14 for notes and resources

Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC). <a href="http://ocdevel.com/mlg/14" rel="nofollow" target="_blank">ocdevel.com/mlg/14</a> for notes and resources

MLG 014 Shallow Algos 3

Performance evaluation &amp; improvement. ocdevel.com/mlg/15 for notes and resources

Performance evaluation &amp; improvement. <a href="http://ocdevel.com/mlg/15" rel="nofollow" target="_blank">ocdevel.com/mlg/15</a> for notes and resources

MLG 015 Performance

Can AI be conscious? ocdevel.com/mlg/16 for notes and resources

Can AI be conscious? <a href="http://ocdevel.com/mlg/16" rel="nofollow" target="_blank">ocdevel.com/mlg/16</a> for notes and resources

MLG 016 Consciousness

Checkpoint - learn the material offline! ocdevel.com/mlg/17 for notes and resources

Checkpoint - learn the material offline! <a href="http://ocdevel.com/mlg/17" rel="nofollow" target="_blank">ocdevel.com/mlg/17</a> for notes and resources

MLG 017 Checkpoint

Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources

Introduction to Natural Language Processing (NLP) topics. <a href="http://ocdevel.com/mlg/18" rel="nofollow" target="_blank">ocdevel.com/mlg/18</a> for notes and resources

MLG 018 Natural Language Processing 1

Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources

Natural Language Processing classical/shallow algorithms. <a href="http://ocdevel.com/mlg/19" rel="nofollow" target="_blank">ocdevel.com/mlg/19</a> for notes and resources

MLG 019 Natural Language Processing 2

Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources

Natural Language Processing classical/shallow algorithms. <a href="http://ocdevel.com/mlg/20" rel="nofollow" target="_blank">ocdevel.com/mlg/20</a> for notes and resources

MLG 020 Natural Language Processing 3

Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources

Recurrent Neural Networks (RNNs) and Word2Vec. <a href="http://ocdevel.com/mlg/22" rel="nofollow" target="_blank">ocdevel.com/mlg/22</a> for notes and resources

MLG 022 Deep NLP 1

Nice intro

Thank you for your generosity and I  really enjoyed your podcast! I am grateful!

Really well done!

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), 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 <a href="http://ocdevel.com" rel="nofollow" target="_blank">ocdevel.com</a>. Audio is a great supplement during exercise, commute, chores, etc.

Machine Learning Guide

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