Open Source For You — December 2017

http://www.OpenSourceForU.com | OPEN SOURCE FOR YOU | DECEMBER 2017 | 79

Insight Developers

Collecting data: Data plays a vital role in the machine

learning process. It can be from various sources and formats

like Excel, Access, text files, etc. The higher the quality and

quantity of the data, the better the machine learns. This is the

base for future learning.

Preparing the data: After collecting data, its quality

must be checked and unnecessary noise and disturbances that

are not of interest should be eliminated from the data. We

need to take steps to fix issues such as missing data and the

treatment of outliers.

Training the model: The appropriate algorithm is

selected in this step and the data is represented in the form

of a model. The cleaned data is divided into training data

and testing data. The training data is used to develop the

data model, while the testing data is used as reference to

ensure that the model has been trained well to produce

accurate results.

Model evaluation: In this step, the accuracy and

precision of the chosen algorithm is ensured based on the

results obtained using the test data. This step is used to

evaluate the choice of the algorithm.

Performance improvement: If the results are not

satisfactory, then a different model can be chosen to

implement the same or more variables are introduced to

increase efficiency.

Types of machine learning algorithms

Machine learning algorithms have been classified into three

major categories.

Supervised learning: Supervised learning is the most

commonly used. In this type of learning, algorithms produce

a function which predicts the future outcome based on

the input given (historical data). The name itself suggests

that it generates output in a supervised fashion. So these

predictive models are given instructions on what needs to

be learnt and how it is to be learnt. Until the model achieves

some acceptable level of efficiency or accuracy, it iterates

over the training data.

To illustrate this method, we can use the algorithm for

sorting apples and mangoes from a basket full of fruits.

Figure 1: Traditional programming vs machine learning Figure 2: The process of teaching machines

Figure 3: Implementing machine learning

Figure 4: Classification of algorithms

Figure 5: Supervised learning model (Image credit: Google)

Here we know how we can identify the fruits based on their

colour, shape, size, etc.

Some of the algorithms we can use here are the

neural network, nearest neighbour, Naïve Bayes, decision

trees and regression.

Data

Data

Program

Output

Output

Program

Training data for the machine like text files,

SQL databases, spreadsheets etc.

Practical application happens here. It is used to generalize

the real-time data to derive new insights

Actual learning happens here by representing data

in simpler and logical format using algorithm

PERSON

STUDENT TEACHER

ISA

dataCollecting^ the dataPreparing^ the modelTraining^ EvaluationModel^ ImprovementPerformance

Machine

Learning

Algorithm

Expected

Label

Predictive

Model

New Text

Document,

Image,

Sound

Training

Text

Documents,

Images,

Sounds...

features

vector

Labels

Machine Learning

Algorithms

Classification

Reinforcement

(Association

Analysis)

Unsupervised

(Clustering,

Dimensionality

Reduction)

Supervised

(Classification,

Regression/

Prediction)