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Machine Learning Guide

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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.
48 Episodes
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MLG 001 Introduction

MLG 001 Introduction

2017-02-0109:1621

Support my new podcast: Lefnire's Life Hacks 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
MLG 002 What is AI, ML, DS

MLG 002 What is AI, ML, DS

2017-02-0901:04:107

Support my new podcast: Lefnire's Life Hacks 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 & 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 & Mathematical decision making Thomas Bayes: reasoning about the probability of events George Boole: logical reasoning / binary algebra Gottlob Frege: Propositional logic 1832: Charles Babbage & 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 & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> 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 & Simon: Hueristics -> 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 -> AI Winter 90s: Data, Computation, Practical Application -> AI back (90s) Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation Bloomberg, 2015 was whopper for AI in industry AlphaGo & DeepMind
MLG 003 Inspiration

MLG 003 Inspiration

2017-02-1018:273

Support my new podcast: Lefnire's Life Hacks Show notes at ocdevel.com/mlg/3. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.
Support my new podcast: Lefnire's Life Hacks Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources
Support my new podcast: Lefnire's Life Hacks Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources
Support my new podcast: Lefnire's Life Hacks Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources
Support my new podcast: Lefnire's Life Hacks Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources
MLG 008 Math

MLG 008 Math

2017-02-2327:232

Support my new podcast: Lefnire's Life Hacks Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ocdevel.com/mlg/8 for notes and resources
MLG 009 Deep Learning

MLG 009 Deep Learning

2017-03-0451:08

Support my new podcast: Lefnire's Life Hacks 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
Support my new podcast: Lefnire's Life Hacks Languages & 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
MLG 012 Shallow Algos 1

MLG 012 Shallow Algos 1

2017-03-1953:213

Support my new podcast: Lefnire's Life Hacks Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ocdevel.com/mlg/12 for notes and resources
MLG 013 Shallow Algos 2

MLG 013 Shallow Algos 2

2017-04-0955:202

Support my new podcast: Lefnire's Life Hacks Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ocdevel.com/mlg/13 for notes and resources
MLG 014 Shallow Algos 3

MLG 014 Shallow Algos 3

2017-04-2348:121

Support my new podcast: Lefnire's Life Hacks 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
MLG 015 Performance

MLG 015 Performance

2017-05-0741:282

Support my new podcast: Lefnire's Life Hacks Performance evaluation & improvement. ocdevel.com/mlg/15 for notes and resources
MLG 016 Consciousness

MLG 016 Consciousness

2017-05-2101:13:502

Support my new podcast: Lefnire's Life Hacks Can AI be conscious? ocdevel.com/mlg/16 for notes and resources
MLG 017 Checkpoint

MLG 017 Checkpoint

2017-06-0407:532

Support my new podcast: Lefnire's Life Hacks Checkpoint - learn the material offline! ocdevel.com/mlg/17 for notes and resources
Support my new podcast: Lefnire's Life Hacks Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources
Support my new podcast: Lefnire's Life Hacks Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources
Support my new podcast: Lefnire's Life Hacks Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources
MLG 022 Deep NLP 1

MLG 022 Deep NLP 1

2017-07-2949:252

Support my new podcast: Lefnire's Life Hacks Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources
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Comments (40)

Soheil Khdpnh

Perfect one to begin with

Apr 26th
Reply

Bob Lockwood

I love this podcast. My last calculus course was 40 years ago. Learning linear regression from this episode was really inspiring. I see some light. I can't wait for multivariate regression. I taught myself how to program as a young teenager in the late 70's. There is hope.

Feb 28th
Reply

mrs rime

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Jan 16th
Reply

Muhammad Khodaei

It was very informative, very comprehensive. Thank you 🙏

Nov 9th
Reply

n nouria

you explain normalizing totally wrong 🤔

Jan 24th
Reply

Mehdi

That was so informative. Thank you for taking your time and sharing your valuable knowledge.🌹

Jan 2nd
Reply

shahla shahmohamadi

thank you so much :)

Jan 12th
Reply

faeze abdoliNejad

tanks its good episode 🥰❤

Aug 16th
Reply

Amir Anbari

great! 👍👍👍👍💪💪💪

Jun 29th
Reply

Ali Vali

This is a voice course that really worth to hear for everyone who is looking to learn the basics of ML through voice. I enjoyed the course when I was cycling. #Machine_learning

Apr 10th
Reply

sracooper

with very good technical details but easy to follow and creative examples :) looking forward to the new episodes!

Mar 8th
Reply

Vivek_m007

wow! Can't believe this podcast is going to start again, started learning ml using your podcasts thank you for making comeback can't wait to learn more from your podcasts.

Oct 28th
Reply

mh

Great! Many thanks 😊 Wondering what's your opinion on the applicability of NLP and DL after the paper and work by OpenAI on one/few shot learning of NL models

Sep 5th
Reply

Zeon X

thanks for your courses

Aug 8th
Reply

Ru m

Love the show. thank you

Jul 29th
Reply

Nikhil Parmar

that's really inspiring and instilling fear 😆

Jul 18th
Reply

Nourhan Slem

this podcast is awesome as a start in machine learning covering all points clearly and as u said the best of this podcast no interviews.it is simple audio course and that's what I was searching for. thanks from egypt 💙

Dec 5th
Reply

Prateek Kumar

really awesome material,i come back and listen to this again from time to time. Thanks for this.

Aug 3rd
Reply

Sam Osborne

good work mate this is fantastic.. very informative and has helped me get concepts that I couldn't otherwise through self study.. I what to give you 10$ in return

Jun 18th
Reply

Alexandre Shimono

Hey, I started listening to your podcast recently, and noticed you haven't published any new episode over the last year or so. Any chances you will come back some day? PS: high-quality material, really appreciated it!

May 25th
Reply