The document discusses various machine learning concepts including artificial intelligence, machine learning, deep learning, neural networks, supervised vs unsupervised learning, naive Bayes classifier, genetic algorithms, and sentiment analysis. It provides examples and code snippets for implementing different machine learning algorithms. The overall agenda is to provide a basic introduction to machine learning topics and algorithms to help reduce intimidation around the field.

1. Learning Machine Learning
A little intro to a (not that complex) world

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About Me

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Our Agenda for today…
• AI vs ML
• Deep Learning &
Neural Networks
• Supervised vs
unsupervised
• Naïve Bayes Classifier
• Genetic Algorithms

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Artificial Intelligence
Artificial intelligence (AI)
is intelligence exhibited by machines.
In computer science, the field of AI research
defines itself as the study of "intelligent
agents": any device that perceives its
environment and takes actions that maximize
its chance of success at some goal.

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Artificial Intelligence
“takes actions that maximize
its chance of success at some
goal”

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Examples in real life

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Machine Learning
Machine learning (ML) is the subfield
of computer science that gives "computers the
ability to learn without being explicitly
programmed."

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“Don’t be afraid of artificial intelligence, be
afraid of humanity.”

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Deep Learning &
Big Data
• Explosion of digital data
• Can’t be processed with
traditional methods
anymore

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Neural
Networks
• Breaking big problems
in small layers

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Supervised
Learning
• Requires feedback
• Starts with nothing
and increases its
understanding
• Useless if the data
is of bad quality
• Use cases:
• Classification

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Unsupervised
Learning
• There is no feedback
• Good in the case of no right or
wrong answer
• Helps to identify patterns or data
structures
• Use case:
• You might also be interested
in…
• Grouping customers by
purchasing behaviors

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The Naïve Bayes Classifier

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Bayes Theorem
• 𝑃 𝐴 𝐵 =
% 𝐴 𝐵 % &
% '

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Bayes Theorem
• 𝑃 𝐴 𝐵 =
% 𝐵 𝐴 % &
% 𝐵 𝐴 % & ( )*% 𝐵 𝐴 )*% &

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Bayes Theorem
• 𝑃 𝐴 𝐵 =
∏ % 𝐴 𝑊-
.
/01
∏ % 𝐴 𝑊-
.
/01 ( ∏ )*% 𝐴 𝑊-
.
/01

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Bayes Theorem
• 𝑃 𝐴 𝐵 = 𝑊2∱

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Naive Bayes
Classifier
• Let’s look at a concrete
example.
• You never know what
you’re gonna get

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Probability that a chocolate has nuts
Nuts No Nuts
Round 25% 75%
Square 75% 25%
Dark 10% 90%
Light 90% 10%

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1
4 𝑷𝒊
𝒏
𝒊8𝟏
0.225 0.075

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1
4 𝑷𝒊
𝒏
𝒊8𝟏
0.225 0.075
𝑃 🌰 =
0.225
0.225 + 0.075
= 0.75 = 75%

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Naïve Bayes Classifier in code
var Classifier = function() {
this.dictionaries = {};
};
Classifier.prototype.classify = function(text, group) {
};
Classifier.prototype.categorize = function(text) {
};

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Sentiment
Analysis
• Not Machine Learning
• Uses classifiers and
AFINN-165 (and
emojis)

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Sentiment
Analysis
• Javascript:
• npm install sentiment
• PHP:
• composer require
risan/sentiment-
analysis

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Genetic
Algorithm
• Awesome shit!

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Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep a few random
individuals
• Introduce random mutations
• Randomly create ”children”
• Magically end up with a valid
solution

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Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep a few random
individuals
• Introduce random mutations
• Randomly create ”children”
• Magically end up with a valid
solution

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Genetic Algorithm
Credit: AutoDesk
https://autodeskresearch.com/projects/Dreamcatcher

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https://www.youtube.com/watch?v=yci5FuI1ovk

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Genetic Algorithm in code
//Declare Consts
function randomInt(min, max) {…}
function random(min, max) {…}
function randomIndividual() {…}
function randomPopulation(size) {…}
function fitness(individual) {…}
function sortByFitness(population) {…}
function mutate(population) {…}
function reproduce(father, mother) {…}
function evolve(population) {…}
function findSolution() {
var population = randomPopulation(POP_SIZE);
var generation = 0;
while (fitness(population[0]) > CLOSE_ENOUGH) {
generation++;
population = evolve(population);
}
return {solution: population[0], generations: generation};
}
var sol = findSolution();

39. Impact of parameters on Genetic Algorithms

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What did we learn?
• Machine Learning and Artificial Intelligence
• Big Data and Deep Learning
• Supervised vs unsupervised
• Basic Algorithms
• Naïve Bayes Classifier
• Sentiment Analysis
• Genetic Algorithm
• Hopefully, you don’t feel intimidated by ML anymore

41. Presented By
JOEL LORD
PHP Tour Nantes, May 19th 2017
@joel__lord
joellord
Thank you
https://joind.in/talk/9e7e2

42. Presented By
JOEL LORD
PHP Tour Nantes, May 19th 2017
@joel__lord
joellord
Questions?
https://joind.in/talk/9e7e2