Get Close with Machine Learning

We know that we are currently in the Industrial Revolution 4.0, which is focusing on the Internet of Things (IoT) using Artificial Intelligence (AI) so that machines will run like robots or can run automatically (Automation Systems). This makes the resulting data more relevant because, later, the industry will produce large amounts of data. Therefore, the data will need to be analyzed immediately (Quality Analysis) to check data such as:

1. Is there anything that will happen after then?
2. Is there knowledge that can be generated or not?
3. Can the data be used as a prediction for the future or not?
   However, not only machine learning/deep learning/TensorFlow but also data management processes, it is essential to know the data information first so it can be used for other methods, such as utilizing metadata.
   Therefore, why do we need Machine Learning?

Previously, we needed to know that Machine Learning is a field that is part of Artificial Intelligence (AI) which is used so that computer systems can automatically learn from data by themselves without being given programming instructions. Meanwhile, Artificial Intelligence (AI) is a branch of computer science that emphasizes the development of machine intelligence, patterns of thinking, and working like humans. It usually requires complex and sophisticated reasoning processes and knowledge.

Read also : Characteristic of Big Data

Thus, Machine Learning is needed to dig up data and provide information as accurately as possible in the form of:

1. Description: Displays data patterns for analysis and problem finding.
2. Prediction: Make predictions in the form of values, probabilities, and data and then recommend them as decision-making tools or automatically used by the system.
   In addition, please note that the goal:
3. Collect, extract, query, clean, and collect data for analysis.
4. Perform visual and statistical analysis of the data.
5. Build, implement and evaluate data science problems using appropriate models and algorithms.
6. Using appropriate data visualization tools to provide an overview of the analysis process results.
7. Make a clear report that can be reproduced to stakeholders.
8. Identify Big Data problems and understand how distributed systems and parallel computing technologies can solve these challenges.
9. Apply questioning, modeling, and validation of problem-solving processes to datasets from various industries to provide insights into problems from real-world solutions.

Read also : What is Machine Learning?

The following is the process of Machine Learning.

1. Data Collection: Collecting the raw data required for problem-solving. Data can be in the form of videos, images, text, etc., on a large or small scale.
2. Data Preparation: Also known as data pre-processing. The process of cleaning data, looking for data with errors or duplicate data, incomplete values, transforming data, to data discretization. No quality data! Data cleaning-Data integration-data transformation-data reduction-data discretization
3. Data Analysis: Machine Learning Algorithms will perform in-depth analysis to search for patterns and find knowledge from data.
4. Data Visualization: Techniques for presenting data visually through graphs, charts, and maps so that the appearance is attractive but still informative.
5. Evaluation & Validation: Measuring the model’s performance to help optimize the model’s parameters so that it is more accurate and effective.

We need to know that Machine Learning is different from traditional programming.
Traditional programming requires data as input and programs made to produce output. Meanwhile, Machine Learning only requires information as input and an example of the desired result. Then the system will find the calculation program by itself.