What is Machine Learning?

We are now in the Industrial Revolution 4.0. It focuses on the Internet of Things (IoT) using artificial intelligence (AI) to enable machines to run like robots or automatically (automation systems). This makes the resulting data more relevant because the industry will produce much data later. Therefore, data must be analyzed immediately (quality analysis) to see data such as:

1. What happened next?
2. Is there any knowledge that can be generated?
3. Can the data be used to predict the future?
   But it is important to know data information for machine learning/deep learning/TensorFlow and data management processes such as using Metadata.
   So why do we need machine learning?

Before doing this, we should know that machine learning is a branch of artificial intelligence (AI) that enables computer systems to learn from data without being programmed. Artificial intelligence (AI), on the other hand, is a branch of computer science that focuses on developing machine intelligence, thinking, and working like humans. It usually requires complex and sophisticated thought processes and knowledge.

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Therefore, machine learning is needed to extract data and provide information as accurately as possible in the form of:

1. Description: Displays data patterns for analysis and problem finding.
2. Prediction: Predictions in the form of values, probabilities, and dates are to be recommended as a decision aid or used automatically by the system.

Moreover, note the purpose:

1. Collect, extract, query, clean, and collect data for analysis.
2. Perform visual and statistical analysis of the data.
3. Create, implement, and evaluate data science problems using appropriate models and algorithms.
4. Use appropriate data visualization tools to provide an overview of the analysis process results.
5. Prepare clear and reproducible reports for stakeholders.
6. Identify big data problems and understand how distributed systems and parallel computing techniques can address these challenges.
7. Apply problem-solving, modeling, and process validation questions to various industry data sets to gain insight into problems from real-world solutions.

The machine learning process is as follows.

1. Data Collection: Collecting the raw data needed to solve a problem. Data can be in various formats, large and small, such as videos, images, and text.
2. Data preparation: Also called data preprocessing. The process of cleaning data, error or duplicate data, searching for incomplete values, and data transformation to data discretization. Lost quality data! Data Cleaning – Data Integration – Data Transformation – Data Reduction – Data Discretization
3. Data Analysis: Machine learning algorithms perform an in-depth analysis by searching for patterns and discovering knowledge from data.
   4 Data Visualization: A technique for presenting data visually through graphs, charts, and maps in a visually attractive yet informative way.
4. Evaluation and Validation: Measuring the model’s performance to optimize the model’s parameters to make it more accurate and effective.

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You should know that machine learning is different from traditional programming. You can see this difference in the image below:

Traditional programming takes data as input, and programs are built to produce output. On the other hand, machine learning only requires data as input and an example of the desired result. Then the system will search for its calculator program.

I hope this helps!!