Artificial Intelligence

Getting to Know Machine Learning and Its Implementation

April 8, 2022
Getting to Know Machine Learning and Its Implementation

In recent years, many have started to study Machine Learning. This cannot be separated from the development of computing technology and data storage that is getting cheaper. But not everyone understands what machine learning is.

What is Machine Learning?

By definition, machine learning or machine learning is the science or study of algorithms and statistical models used by computer systems to perform certain tasks without explicit instructions. Machine learning relies on patterns and inferences. To get these patterns and conclusions, machine learning algorithms generate mathematical models based on sample data which is often referred to as 'training data.'

What is the Difference Between Machine Learning and Artificial Intelligence (AI)?

This AI refers to a computer programming procedure (machine) to take something rational. Is that rational? Rational is the basis for making decisions

For example, AI is used to check whether certain parameters in a program behave Normal. For example, a machine can raise an alarm if a parameter says 'X' crosses a certain threshold which in turn can affect the result of the related process.

Use of AI in Machine Learning

Machine Learning is a subset of AI where machines are trained to learn from past experiences. Past experiences are developed through the collected data, then combined with algorithms (such as Naïve Bayes, Support Vector Machine (SVM)) to give the final result.

What is the difference between Machine Learning and Statistics?

Statistics is a branch of mathematics that utilizes data from either an entire population or a sample to perform analysis and present conclusions. Several statistical techniques used are regression, variance, standard deviation, conditional probability and others.

What is the Difference Between Machine Learning and Deep Learning?

Deep Learning is associated with an Artificial Neural Network (ANN) algorithm that uses the concept of the human brain to facilitate the modeling of arbitrary functions. ANN requires a large amount of data and this algorithm is very flexible in terms of generating multiple outputs simultaneously.

What is the Difference Between Machine Learning and Data Mining?

Data Mining is used to find specific information, while Machine Learning concentrates on performing certain tasks. As an example to help with the difference between Machine Learning and Data Mining, teaching someone how to dance is Machine Learning, whereas using someone to find the best dance center in town is Data Mining.

How Does Machine Learning Work?

Machine Learning involves a structural process by which each stage builds a better version of the machine. For simplicity, the Machine Learning process can be divided into several parts:

Collecting data

Raw data can be Excel, Access, text files and others. This step forms the basis for future learning. The more variety, density and volume of relevant data, the better the prospects for machine learning.

Prepare data

Each analytical process evolves with the quality of the data used. We need to spend some time determining data quality and then take steps to fix problems like data loss and others.

Train a model

This step involves selecting the appropriate algorithm and data representation in the form of a model. The prepared data is divided into two parts: train and test. The first part (training data) is used for model development. The second part (test data), is used as a reference.

Evaluating the model

To test the accuracy, the second part of the data (test data) was used. This step determines the accuracy in the selection of the algorithm based on the test results. A better test to check the accuracy of the model is to look at its performance on data that was not used at all during modeling.

Improve the performance

This step may involve selecting a different model or introducing more variables to increase efficiency. That is why it takes a lot of time for data collection and data preparation.

Supervised Learning / Predictive Models

This model is used to predict future outcomes based on historical data. Predictive models are usually given clear instructions from the start as to what needs to be learned and how it needs to be learned. This learning algorithm is called Supervised Learning.

For example: Supervised Learning is used when a marketing company is trying to find out which customers are likely to switch or look for other suppliers. This algorithm can also be used to predict the possibility of hazards such as earthquakes, tornadoes and others, with the aim of knowing the Total Insurance Value. Some examples of algorithms used are: Nearest Neighbor, Naïve Bayes, Decision Tree, Regression, and others.

Unsupervised Learning/Descriptive Model

This model is used for training where no target is set and no other factor is important. As an example of using this unsupervised learning model, if a retailer wants to know what combination of products consumers tend to buy more often. In the pharmaceutical industry, it is used to predict which diseases may co-occur with diabetes. An example of the algorithm used in this model: K-Means Clustering Algorithm.

Reinforcement Learning (RL)

This model is an example of machine learning where machines are trained to make specific decisions based on business needs with the primary goal of maximizing efficiency (performance). The idea of ​​Reinforcement learning is that the machine/software trains itself continuously based on the environment it influences, and applies the enriched knowledge to solve business problems. This continuous learning process ensures less human involvement and thus saves a lot of time.

To distinguish between Supervised Learning and Reinforcement Learning, for example, a car uses Reinforcement learning to make decisions on which route to take, what speed to drive, where some of these questions are decided after interacting with the environment.

The Use of Machine Learning

Google and Facebook are two examples of companies that use Machine Learning extensively to push their respective ads to relevant users. Other examples of using Machine Learning are:

Banking & Financial Services

Machine Learning can be used to predict which customers are likely to default on loans or credit card bills. This is very important because Machine Learning will help banks to identify customers who can be given loans and credit cards.


Used to diagnose deadly diseases (e.g. cancer) based on the patient's symptoms and calculate it with the latest data from the same type of patient.


Used to identify products that sell more often (fast moving) and products that are slow. This helps decide what types of products to display or remove from the shelves. In addition, Machine Learning algorithms can be used to find two or more products that are sold together. This is done to stimulate customer loyalty initiatives which in turn help retailers to develop loyal customers.

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