Chapter 1 : Quick Introduction

Select few pilots (say 2) in parallel, not in mission-critical areas but in smaller ones, in your specific industry, where a win is likely in a few months time, and where successes will win over skeptics. - Andrew Ng in How to Choose Your First AI Project

Valley ^{Image credit: Chromecast}

In traditional programming we start with data and hard-coded rules to apply on the data to get answers.
This style of programming can't bring answers easily for problems like:
- predicting a type of a cat in the given animal picture

Assume you need to write a program find out the given animal is cat or dog. Traditional way will be something like this:

def detect_colors(image):
# lots of code

def detect_edges(image):
# lots of code

def analyze_shapes(image):
# lots of code

def guess_texture(image):
# lots of code

def define_animal():
# lots of code

def handle_probability():
# lots of code

So, we will be writing lot of hard-corded rules!

It would be great if write a algorithm (say Classifier) which can figure out rules for us based on the data we provided (data-driven rules), so we do not have to write those rules by hand.

So it is trained on the Data and rules are written based on the provided data

So when we provide:

Input: Data (say cat's image) : cat image
The program takes in the given the cat image
Output: Predicted animal name - Persian Cat with probability 89.178%

As shown in the demo below, the user provides a image of the cat, the application predicts type of the cat in that image with a confidence (probability) with help of a Machine Learning Model.

Cool?

Image Recognition - 1

To build this kind of solution using traditional programming, we may have to write too many rules or sometimes this problem is not easily solvable by our traditional programming. Here comes our hero Machine Learning to our rescue us!

What is special about Machine Learning ?

How long it will take to write the code based on hard-coded rules for this task:

Solving Rubik’s Cube with a single Robot Hand using our traditional programming?

Steps in the ML

Goal: Create an accurate Model that answers our questions most of time

Step-1 - Gathering Data

To train a ML Model we need to:
- Collect data to train on

Step-2 - Data Preparation

Load the data and visualize it
Check for data errors and data imbalances
Split the data into 2 parts
- 1. Training Data (80%)
- 1. Testing Data (20%)

Step-3 - Choosing a Model

In our case, we can use a linear model.

Step-4 - Training the Model

Model

\(y = mx + b\)
\(x\) is the input
\(y\) is the output (prediction)

The values we are to going to adjust the training are:

\(m\) (weight) and \(b\) (bias)
Start the training by initializing \(m\) (weight) and \(b\) (bias) with some random values
At the beginning, the Model will perform very poorly
- We compare the model's output \(y\) with what it should have produced (target value of y)
- We will adjust values of \(m\) (weight) and \(b\) (bias) so that we get more accurate predictions on the next time around
- This error correction repeats...
  - Each iteration updates \(m\) (weight) and \(b\) (bias) - called one training step
- We will stop the training once we got the good accuracy (low error)

Step-5 - Evaluating the Model

We can check the fitness of our Model using Evaluation
We test our Model against the Testing Data we created in Step-2
- We are testing the model against data the Model has not seen yet (simulating the real-world situation)

Step-6 - Parameter Tuning

Parameters (AKA hyper-parameters) we can tune:

How many times we run through the training dataset?
Learning Rate
- How far we did the error correction based on the information from the previous training step

These parameters determine:

Accuracy of our Model
How long it takes to train the Model

How we initialized the Model affects the Model training time - Random values or - Zero values

Machine Learning for Everyone!