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Machine Learning Guide
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Machine Learning Guide

Author: OCDevel

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Teaches the high level fundamentals of machine learning and artificial intelligence. I teach basic intuition, algorithms, and math. I discuss languages and frameworks, deep learning, and more. Audio may seem inferior, but it's a great supplement during exercise/commute/chores. Where your other resources provide the machine learning trees, I provide the forest. Consider me your syllabus. At the end of every episode I provide high-quality curated resources for learning each episode’s details.
30 Episodes
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001 Introduction

001 Introduction

2017-02-0100:32:0556

Teaches the high level fundamentals of machine learning and artificial intelligence. I teach basic intuition, algorithms, and math. I discuss languages and frameworks, deep learning, and more. ocdevel.com/mlg/1 for notes and resources
002 What is AI / ML

002 What is AI / ML

2017-02-0900:31:3732

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

003 Inspiration

2017-02-1000:17:4714

Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear. ocdevel.com/mlg/3 for notes and resources
004 Algorithms - Intuition

004 Algorithms - Intuition

2017-02-1200:22:1522

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

2017-02-1600:33:3023

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

006 Certificates & Degrees

2017-02-1700:33:396

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

2017-02-1900:33:5311

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

2017-02-2300:28:1311

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

2017-03-0400:50:5515

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
010 Languages & Frameworks

010 Languages & Frameworks

2017-03-0700:45:0515

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
011 Checkpoint

011 Checkpoint

2017-03-0800:07:454

Checkpoint - start learning the material offline! ocdevel.com/mlg/11 for notes and resources
012 Shallow Algos 1

012 Shallow Algos 1

2017-03-1900:53:174

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

2017-04-0900:55:138

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

2017-04-2300:48:076

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

2017-05-0700:41:243

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

016 Consciousness

2017-05-2101:13:456

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

017 Checkpoint

2017-06-0400:07:00

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
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Comments (24)

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

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

Ivan Raszl

Well done!

Jun 29th
Reply

Martin Larsson

Super helpful and easy listening. You rock!

Jun 17th
Reply

Mari Subramanian

thanks for this. this is gold

May 3rd
Reply

Siva Sukumar Reddy

so far, the best source for machine learning

Apr 13th
Reply

Scott Parker

This has been a really helpful podcast, thank you for interesting your time!

Mar 22nd
Reply

Sankarshan Sen

thanks for the podcast, brilliant work

Feb 6th
Reply

g3n c0d3r

Simply amazed at how well I was able to visualize the whole concept even though it's just audio. Keep up the good work!

Jan 29th
Reply

Tim O'Hagan

Really great explanations! Very well done!

Jan 23rd
Reply
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