Complete exposure to future technology synchronization which synchronizes Artificial Intelligence with Machine Learning and Deep learning with various forms of good, efficient and high quality smart electronic networks. Machine learning Machine learning is a branch of computer science with a focus on developing a system that is able to learn on its own without having to be repeatedly programmed by humans. However, before producing a data result from object behavior, Machine Learning requires initial data as material to be studied.
Machine learning (ML) is a learning machine designed to be able to learn without human direction. Machine learning is a branch of artificial intelligence (AI) or artificial intelligence.
Machine learning is often used for various purposes. Machine learning also has the ability to be able to obtain its own data and then study it so that it can perform certain tasks. This machine learning is based on the sciences of mathematics, statistics, data mining, and others
The initial role of data is very important as the first step in Machine Learning to produce output. It is used as an initial exercise or trial of Machine Learning. After passing the initial trials, Machine Learning will be able to solve problems without being explicitly programmed.
Deep Learning is a part of machine learning where the algorithm is able to understand patterns with high accuracy based on very large data through various complex variables. Deep Learning on the other hand is one of the implementation methods of Machine Learning which aims to imitate how the human brain works using Artificial Neural Network or artificial reasoning network. Deep Learning with a number of algorithms as "neurons" will work together in determining and digesting certain characteristics of a data set. Programs in Deep Learning usually use more complex capabilities in learning, digesting, and also classifying data.
One of the main differences between Machine Learning and Deep Learning is performance as the amount of data increases and how to solve problems. Deep Learning algorithms are used to create artificial neural networks that are not capable of optimally processing small amounts of data. This is because Deep Learning algorithms require large amounts of data and are able to solve the problem as a whole from start to finish without the need to separate it into several parts.
Meanwhile, Machine Learning algorithms are capable of processing smaller amounts of data. And to solve the problem, it is recommended to break it into several parts so that it can be solved separately, and the solutions are combined to get a complete result.
Every technological sophistication is designed to make human work easier. Likewise with machine learning, machine learning has its own way of working that varies according to the technique to be used.
The main concept of machine learning remains the same, which includes data collection, data cleaning, data exploration, data selection, technique selection. provide training on models, and evaluate Machine learning results.
We often encounter the application of machine learning in everyday life for various purposes. Some examples of the application of machine learning include:
marketplace recommendations in the online shopping system, where one of the data is obtained from search history
categorization of email, whether it is included in the category of updates, social, promotions, spam, and others.
facial recognition, often used in security systems
search engine, provides search suggestions in the google search engine .
Machine learning in its application has penetrated into various fields. Things like transportation applications, financial services, education, health, and social media are examples of machine learning in everyday life.
real examples of machine learning in the 21st century:
Google search autosuggest
Youtube, Netflix and Spotify video recommendations.
The Home feature on Facebook, Instagram, and Twitter.
Facebook and IG ads: matching product ads
Spam e-mail filter.
Speech to text
Virtual assistants: Cortana, Siri, Google Now
Self-driving car .
In the 21st century and maybe the 22nd century machine learning technology will begin to be designed with Artificial intelligence network technology so as to make a process of arranging existing servers or cloud engines to be connected and designed not only for learning electronic machines, but also for high-tech electronic machines