AI Machine Learning Complete Course: for PHP & Python Devs

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AI Machine Learning – The Complete
Course: for PHP & Python Developers
https://www.udemy.com/ai-machine-learning-complete-
course/

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Section 1
Understanding AI & Machine Learning

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Artificial IntelligenceIt is Everywhere
Top Trending Technology
Used Everywhere
• Websites
• Mobile Apps
• Chat-bots
• Search Engines
• & Much More!

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1956
Founded as an
Academic
Discipline
Studied By
Researchers
1943
Artificial IntelligenceHistory

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What is Artificial Intelligence?Definition
Any technology that makes
machines process or respond to
data in human like ways, so the
machine can simulate human
behavior such as learning, and
problem solving.

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Artificial Intelligence ApproachesThere are many approaches
Machine Learning
Rule-Based AI
a.k.a. Symbolic Reasoning
a.k.a. GOFAI
There are many approaches to mimic human
intelligence, but the most common are

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Logic & Rule Based AIThe Good Old-Fashioned AI
Set of rules, logic, like a nested “if .. Else .. “
statements
1. If someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,
2. if a move "forks" to create two threats at once, play that move. Otherwise,
3. take the center square if it is free. Otherwise,
4. if your opponent has played in a corner, take the opposite corner. Otherwise,
5. take an empty corner if one exists. Otherwise,
6. take any empty square.
Example – Tic-tac-toe

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Machine LearningThe dominant approach for AI today
A computer program finds patterns in data and
create its own rules. Basically Learns from
historical data
There are four types of machine learning algorithms:
supervised, semi-supervised, unsupervised and
reinforcement
Machine Learning can be done using many
algorithms

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Section 2
Machine Learning Types & Algorithms

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Machine Learning TypesJust as a reference
Supervised
Learning
Classificatio
n
Regression
Forecasting
Semi-
supervised
Learning
Unsupervised
Learning
Clustering
Dimension
Reduction
Reinforcement
Learning

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Machine Learning AlgorithmsJust as a reference
1. Naïve Bayes
2. K Means
3. Support Vector Machine
(SVM)
4. Linear Regression
5. Logistic Regression
6. Decision Trees
7. Random Forests
8. Nearest Neighbors
9. Neural Networks
Simple Example of a Neural Network

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What Algorithm Should I Use?
The Cheat Sheet

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What to ConcludeMind Map
AI (Artificial Intelligence)
Machine Learning
Neural
Networks
SVM ...
Rule-Based
AI ...

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Section 3
Coding Example: Dominant Color
Detector (Neural Networks Algorithm)

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Let’s Code
The Fun Part 

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Dominant Color DetectorReal world example for an AI Application in PHP
Our Neural Network
Each Machine Learning Program
Generally Consists of:
• Training
• Prediction

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Dominant Color DetectorReal world example for an AI Application in PHP
For this example, we want to give the AI program the color code for any color, and it
gives us what is the dominant primary color in that color. Pretty cool.
For Example, the dominant color for this is blue

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Dominant Color Detector - PlanningLet’s plan our code
train predict
1. Build the network
2. Read test data
3. Train the network
4. Save the trained network to
a file
1. Restore the trained network from
the file
2. Pass a completely new input
3. Observe the network output

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Dominant Color Detector - PlanningThe Network
R
G
B
Three Inputs
Activatio
n
function
PReLU
Activatio
n
function
Sigmoid
2 Hidden Layers – 4 Neurons Each
Output is
either “red”,
“blue”, or
“green”

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PHP-MLA wonderful Machine Learning Library
For this example, we will use a library called PHP-ML (https://github.com/php-ai/php-
ml), this library is a fresh approach to Machine Learning in PHP. Algorithms, Cross
Validation, Neural Network, Preprocessing, Feature Extraction and much more in that
library.
Requirements:
1. PHP7.1+
2. Apache
3. Composer

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Code Lab – Dominant Color Detector
Fingers Crossed

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Artificial IntelligenceThink of the Possibilities
YES! You have now done your
first complete AI Machine
Learning application, and using
Neural Networks!
You Can Do More!

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Section 4
Coding Example: Language Detection
(Support Vector Machine Algorithm)

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Language DetectionReal world example for an AI Application in PHP
Sample Data:
"sentence","language"
"Hello, do you know what time the movie is tonight?","english"
"Vérifiez la batterie, s'il vous plaît.","french"
"A che ora sara' la prossima raccolta?","italian"
CSV

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Language DetectionFeature Extraction
Text, Image, Video, or
Voice
Numerical
Representation

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Brown
Eyes
Black
Eyes
Long
Ears
Black
Fur
White
Fur
Image
1
1 0 1 1 0
Image
2
1 0 0 0 1
Image
3
0 1 1 1 0
Image
4
0 1 0 0 1

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Text Data Count Vectorizer TFIDF Transformer Numerical
Vectors
Word Counter
We will use Kernel SVM Algorithm

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Code Lab – Language Detector
Fingers Crossed

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Applications!Tweeter Analysis

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Python & PHPWhat we can do!

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Python & PHPWhat we can do!
Python was build in a light core, and lots of packages
PHP is a powerful web development language
exec("python
mypythonscript.py",$output);
var_dump($output);
Python REST API

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Python SyntaxCrash Course
Python files doesn’t start with anything special, just
write code directly “unlike php which requires <?php
at the beginning or something similar”. And the
Python files have the extension .py
Python files cannot run from the browser directly, it
needs a web framework, we are not going to
explain this for now, but the way you can use
Python easily “and the most common” is from
command line like this “python mypythonscript.py”

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You can download and install Python from the official
Python website (https://www.python.org/downloads/)
and the install is very easy, after that you can just go
to command line navigate to your Python file and run
it with the “python” command. More info on
installation from here
(https://realpython.com/installing-python/)

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No semi column at the end of the statement, it is
optional, but strongly recommended not to use it
New line indicates a new statement
For comments Python uses “#” at the beginning of
each line, it doesn’t have multi-line comments

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Some basic Python data types are Numbers, String, Tuple
“like array but immutable”, List, Dictionary. The first three
are immutable. List and Dictionary are mutable. List is
ordered, but Dictionary is not
In PHP you include files with “include, require, include_once,
require_once”, in Python you can include using “import,
__import__(), and importlib.import_module()” (for example
“import sqlite3”, no paths here, once you install the module,
you can import it like that)
Whereas PHP uses NULL, Python uses None which can be
checked by “if foo is None”

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Print Statement
a. In PHP
print("Hello World");
b. In Python
print("Hello World")

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Echo Statement
a. In PHP
echo "Hello", " World";
b. In Python (no echo inPython)
print("Hello", "World")

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SwitchStatement
a. In PHP
$animal = "duck";
switch ( $animal ) {
case "duck": echo "two legs"; break;
case "cow": echo "four legs"; break;
default:
echo "don't know";
}
b. In Python (there is no switchstatement in Python, but you can do it withif .. elif .. else, elif here = elseif in PHP) (Adding a column at
the end of condition statements or for loops, examine below)
animal = "duck"
if animal == "duck":
print("two legs")
elif animal == "cow":
print("four legs")
else:
print("don't know")
Instead of “switch” inPython you canalso use dictionaries as follows, much easier:
animal = "duck"
legs = { "duck": "two legs", "cow": "four legs" }
if animal in legs:
print(legs[animal])
else:
print("don't know")

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Multi-line IF
a. In PHP
$x = 1;
if ($x == 1) {
$a = 1;
$b = 2;
$c = 3;
}
b. In Python (no { or }, just an indentation would indicate the if scoop, indentation
and spacing is very important in Python)
if x == 1:
a = 1
b = 2
c = 3

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Conditional Expressions
a. In PHP
$a = 7;
$b = 10;
echo ($a < $b) ? "a is less": "not";
b. In Python
a = 7
b = 10
print ("a is less" if a < b else "not")

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For Loops
a. In PHP
for ( $i = 0; $i < 10; $i++ ) {
echo $i;
}
b. In Python (range provides the condition out of the box) (the second parameter of
the print here means it will not print end of line after the printable because it
does by default)
for i in range(0,10):
print(i, end="")

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While Loops
a. In PHP
$i = 0;
while ($i < 10) {
echo $i;
$i++;
}
b. In Python (There is no ++ operator in Python, insteadit has +=)
i = 0
while ( i < 10 ):
print(i, end="")
i += 1

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Loop Through List
a. In PHP
$myList = array("duck", "cow", "sheep");
foreach ($myList as $animal) {
echo($animal);
}
b. In Python
myList = ["duck", "cow", "sheep"]
for animal in myList:
print(animal)

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Functions
a. In PHP
function main() {
sayHello("John");
}
function sayHello($person) {
echo ("Hello " . $person);
}
main();
b. In Python (it is easyto define functions) (InPython, you have anextra check, the __name__ will be = to “__main__” only if the file
itself was executed and not imported from another file, so you can have more control on when to run the mainfunction or not)
def main():
sayHello("John")
def sayHello(person):
print("Hello", person)
if __name__ == "__main__":
main()

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Classes
a. In PHP
class Person {
var $name;
function __construct($name) {
$this->name = $name;
}
function sayHi() {
echo('Hello, my name is ' . $this->name);
}
}
$p = new Person('John');
$p->sayHi();
b. In Python (self is like $this, but you need to pass it in every function)
class Person:
def __init__(self, name):
self.name = name
def sayHi(self):
print('Hello, my name is', self.name)
# this is outside the class, notice indentation changed.
p = Person('John')
p.sayHi()

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Section 6
Coding Example: Simple Neural
Network (Built from scratch without
libraries!)

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Simple Neural Network - PythonTraining Machine Learning Example

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Simple Neural Network - TrainingTraining Algorithm
L1 - Input
L2 - Output
Random
Weight
Random
Weight
Random
Weight
1. Forward Propagation
2. Back Propagation

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Output Calculation – ForwardLet’s do the Math
1. Forward Propagation
2. Normalize Output (Between 0 and 1)
Sigmoid
3. Together, the output becomes

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Weight Adjustment – BackwardLet’s do the Math
2. Backward
Propagation
1. Calculate Error (output is predicated output by the
network)
𝑒𝑟𝑟𝑜𝑟 = 𝑜𝑢𝑡𝑝𝑢𝑡 𝑓𝑟𝑜𝑚 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 − 𝑜𝑢𝑡𝑝𝑢𝑡

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numpyPython’s Math
• exp— the natural exponential
• array — creates a matrix
• dot — multiplies matrices
• random — gives us random numbers
training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = array([[0, 1, 1, 0]]).T
0 0 1
1 1 1
1 0 1
0 1 1
0
1
1
0

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Code Lab – Simple Python Neural Network
Fingers Crossed

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Well Done!Think of the Possibilities
And we have a neural network now done with Python! A simple
one!
What about this one?

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XOR AssignmentFor you to workout!

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Section 7
Important Neural Networks Concepts

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New ConceptsThere are More!
Activation Functions
Sigmoid

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Input Dimension

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Dense Layer & Dropout Layer

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Learning Rate

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Model Loss

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Epoch
Batch
1
Batch
2
Batch
3
...
Batch
n
Batch
Sample
1
Sample
2
Sample
3
...
Sample
n
Epoch & Batch Size

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Training Data vs Validation Data vs Test Data
for each epoch
for each training data instance
- propagate error through the network
- adjust the weights
- calculate the accuracy over training data
for each validation data instance
calculate the accuracy over the validation
data
If the accuracy over the training data set
increases, but the accuracy over the validation
data set stays the same or decreases
exit training
else
continue training

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Training Data vs Validation Data vs Test
Data
Dataset
Count
Testing Part (0.2) Training Part (0.8)
1,000 = 1,000 * 0.2 = 200 = 1,000 * 0.8 = 800
Validation Portion
(0.1)
Training Portion (0.9)
= 800 * 0.1 = 80 = 800 * 0.9 = 720
For example, if we have 1000 data samples

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Section 8
Coding Example: Language Detection
with Google's TensorFlow (Real-world
example)

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TensorFlowLet’s go further!

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Language Detection - TensorFlowA real world Deep Neural Networks Example
Lots of Data
High Accuracy
Language
Detection
Deep Neural Network

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Text Data Count Vectorizer
Standard Scaler
Transformation
Numerical
Vectors
Letter Counter
We will identify: English, French, Spanish, Italian, German, Slovak, Czech

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The DataThanks Wikipedia!
Downloaded from
https://dumps.wikimedia.org/
Then Extracted to txt using
http://medialab.di.unipi.it/wiki/Wikipedia_Extractor
I generated 7 files 1 for each language, 204 MB each!!
I gave you 5 files only

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Code Lab – TensorFlow Language Detection
Fingers Crossed

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Data CountsTo Clarify
Training Data vs Validation Data vs Test
Data
Dataset Count per
Language
Total Dataset
Count (we have
data for 5
languages)
Testing Part (0.2) Training Part (0.8)
250,000
= 5 * 250,000 =
1,250,000
= 1,250,000 * 0.2 =
250,000
= 1,250,000 * 0.8 = 1,000,000
Validation Portion
(0.1)
Training Portion (0.9)
= 1,000,000 * 0.1 =
100,000
= 1,000,000 * 0.9 =
900,000

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AWESOME!We did it, again!
Accuracy
98%
Machine learning train part is from lines 315-330
(just 11 lines of code!) and the predict code is from
396-406 (10 lines of code!) the remaining code is
for:
Data cleaning (custom function)
Text vectorization (custom function)
Text Transformation (from library)
Evaluation, reporting, and plotting

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Machine Learning is FunYou can apply it everywhere!
1. Get training data for your problem.
2. Convert those data into numerical vectors.
3. Train the network.
4. Predict!

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TensorFlow HubDon’t Reinvent the Wheel
https://alpha.tfhub.dev/

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Code Lab – TensorFlow Hub Examples
Fingers Crossed

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Bonus: Section 10
Bonus Section: Building a
Neural Network using Plain
Math

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Building Neural NetworksManually!

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We will walk through a simple example of training a
neural network to function as an “Exclusive OR”
(“XOR”) operation to illustrate each step in the
training process.
input | output
-------------------
0, 0 | 0
0, 1 | 1
1, 0 | 1
1, 1 | 0

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84

85
1 * 0.8 + 1 * 0.2 = 1
1 * 0.4 + 1 * 0.9 = 1.3
1 * 0.3 + 1 * 0.5 = 0.8

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87

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S(1.0) = 0.73105857863
S(1.3) = 0.78583498304
S(0.8) = 0.68997448112

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0.73 * 0.3 + 0.79 * 0.5 + 0.69 * 0.9 = 1.235
S(1.235) = 0.7746924929149283

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Output sum margin of error = target – calculated
Target = 0
Calculated = 0.77
Output sum margin of error = Target – calculated = -
0.77
𝑆′
𝑠𝑢𝑚 =
𝑑𝑠𝑢𝑚
𝑑𝑟𝑒𝑠𝑢𝑙𝑡
𝑑𝑠𝑢𝑚
𝑑𝑟𝑒𝑠𝑢𝑙𝑡
× 𝑡𝑎𝑟𝑔𝑒𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 − 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑟𝑒𝑠𝑢𝑙𝑡 = ∆𝑠𝑢𝑚
Delta output sum = S'(sum) * (output sum margin of error)
Delta output sum = S'(1.235) * (-0.77)
Delta output sum = -0.13439890643886018

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𝐻𝑟𝑒𝑠𝑢𝑙𝑡 × 𝑊ℎ→𝑜 = 𝑂𝑠𝑢𝑚
𝑑𝑂𝑠𝑢𝑚
𝑑𝑤ℎ→𝑜
= 𝐻𝑟𝑒𝑠𝑢𝑙𝑡𝑠
hidden result 1 = 0.73105857863
hidden result 2 = 0.78583498304
hidden result 3 = 0.68997448112
Delta weights = delta output sum / hidden layer results
Delta weights = -0.1344 / [0.73105, 0.78583, 0.69997]
Delta weights = [-0.1838, -0.1710, -0.1920]
old w7 = 0.3
old w8 = 0.5
old w9 = 0.9
new w7 = 0.1162
new w8 = 0.329
new w9 = 0.708
𝑑𝑤ℎ→𝑜 =
𝐻𝑟𝑒𝑠𝑢𝑙𝑡𝑠

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𝐻𝑟𝑒𝑠𝑢𝑙𝑡 × 𝑊ℎ→𝑜 = 𝑂𝑠𝑢𝑚
𝑑𝐻𝑟𝑒𝑠𝑢𝑙𝑡 =
𝑤ℎ→𝑜
𝑆′
𝐻𝑠𝑢𝑚 =
𝑑𝐻𝑠𝑢𝑚
𝑑𝐻𝑟𝑒𝑠𝑢𝑙𝑡
=
1
𝑤ℎ→𝑜
𝑑𝐻𝑟𝑒𝑠𝑢𝑙𝑡 ×
=
𝑤ℎ→𝑜
×
𝑑𝐻𝑠𝑢𝑚 =
𝑤ℎ→𝑜
× 𝑆′(𝐻𝑠𝑢𝑚)
Delta hidden sum
= delta output sum / hidden-to-outer weights * S'(hidden sum
Delta hidden sum
= -0.1344 / [0.3, 0.5, 0.9] * S'([1, 1.3, 0.8])
Delta hidden sum
= [-0.448, -0.2688, -0.1493] * [0.1966, 0.1683, 0.2139]
Delta hidden sum
= [-0.088, -0.0452, -0.0319]

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𝐼 × 𝑤𝑖→ℎ = 𝐻𝑠𝑢𝑚
𝑑𝑤𝑖→ℎ =
𝐼
𝑑𝑤𝑖→ℎ
= 𝐼
input 1 = 1
input 2 = 1
Delta weights = delta hidden sum / input data
Delta weights = [-0.088, -0.0452, -0.0319] / [1, 1]
Delta weights = [-0.088, -0.0452, -0.0319, -0.088, -0.0452, -0.0319]
old w1 = 0.8 new w1 = 0.712
old w2 = 0.4 new w2 = 0.3548
old w3 = 0.3 new w3 = 0.2681
old w4 = 0.2 new w4 = 0.112
old w5 = 0.9 new w5 = 0.8548
old w6 = 0.5 new w6 = 0.4681

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old new
--------------------------
w1: 0.8 w1: 0.712
w2: 0.4 w2: 0.3548
w3: 0.3 w3: 0.2681
w4: 0.2 w4: 0.112
w5: 0.9 w5: 0.8548
w6: 0.5 w6: 0.4681
w7: 0.3 w7: 0.1162
w8: 0.5 w8: 0.329
w9: 0.9 w9: 0.708

96
Well Done!
You have successfully started
your AI journey! Keep going!
I would love to help
Keep in Touch
linkedin.com/in/amrshawqy
twitter.com/amrshawqy

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