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Machine Learning Guide
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Machine Learning Guide

Author: OCDevel

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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.
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001 Introduction

001 Introduction

2017-02-0120:115

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.
002 What is AI / ML

002 What is AI / ML

2017-02-0932:044

Show notes at ocdevel.com/mlg/2. What is artificial intelligence and machine learning? What's the difference? How about compared to statistics and data science? AI history.
003 Inspiration

003 Inspiration

2017-02-1017:41

Show notes at ocdevel.com/mlg/3. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.
Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources
005 Linear Regression

005 Linear Regression

2017-02-1633:401

Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources
Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources
007 Logistic Regression

007 Logistic Regression

2017-02-1934:192

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

008 Math

2017-02-2327:231

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

009 Deep Learning

2017-03-0451:08

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
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
012 Shallow Algos 1

012 Shallow Algos 1

2017-03-1953:172

Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ocdevel.com/mlg/12 for notes and resources
013 Shallow Algos 2

013 Shallow Algos 2

2017-04-0955:131

Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ocdevel.com/mlg/13 for notes and resources
014 Shallow Algos 3

014 Shallow Algos 3

2017-04-2348:07

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
015 Performance

015 Performance

2017-05-0741:242

Performance evaluation & improvement. ocdevel.com/mlg/15 for notes and resources
016 Consciousness

016 Consciousness

2017-05-2101:13:45

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

017 Checkpoint

2017-06-0407:00

Checkpoint - learn the material offline! ocdevel.com/mlg/17 for notes and resources
Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources
Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources
Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources
022 Deep NLP 1

022 Deep NLP 1

2017-07-2949:21

Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources
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CommentsΒ (32)

Amir Anbari

great! πŸ‘πŸ‘πŸ‘πŸ‘πŸ’ͺπŸ’ͺπŸ’ͺ

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 πŸ˜†

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 πŸ’™

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

simon abdou

an awesome and clear summary of RL. Thank you

May 6th
Reply

Eduard Kumpan

I was sceptical if hearing how an algorithm works will make me understand it, but you did a great job explaining it!

Apr 3rd
Reply

Amir Shirazi

professional work and very good quality in all aspects. thanks!

Feb 28th
Reply

Khaing Win

can you make an episode on the latest Google self attention mechanism in neural machine translation?

Dec 31st
Reply

Mahamad El Tanahy

Definitely the best podcast on machine learning. great job!

Oct 29th
Reply

alireza bayat

simply amazing , appreciate for your brilliant podcasts

Oct 9th
Reply

112358 132134

Thank you, dude! Awesome. Your podcasts are really helpful :)

Aug 14th
Reply

Edgar Scott

your descriptions are better than some of the moocs on udemy!!!

Aug 8th
Reply
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