What is machine learning?
As much as technical terms like machine learning, deep learning, Big Data, and artificial intelligence are still somewhat confusing, they are in fact quite distinct. Would you like to master the workings of machine learning? How does this new AI technology work? And what impact does it have on our day-to-day work and in the workplace? Here is a special feature on machine learning to gain a better understanding of how it works and its possible applications in customer relations.
The definition of machine learning
In essence, machine learning is an artificial intelligence (AI) technology that allows a machine to make predictions or automate tasks. This AI technology is based on algorithms that recognize recurring trends or patterns in a database. These patterns can be texts, numbers, images, or videos that are stored digitally.
The distinctive feature of machine learning lies in its ability to learn from this data history and to continuously improve its performance with complete autonomy.
How does machine learning work?
Machine learning consists of training an algorithm to recognize recurring patterns in a database. This training is known as a “machine learning model”. It is based on four steps:
- Preparing a data set to feed the machine learning model and therefore make it autonomous. This data can be labeled to make the program’s work easier and to reduce the emergence of bias in the predictive analysis
- Selecting the algorithm, i.e. the operating method to be applied to the data set. There are different types depending on the application
- Training the algorithm to produce the machine learning model. This is an iterative process that compares the variables that have been executed with those that should have occurred. Once there are no more differences, the training of the algorithm is considered complete
- Using the machine learning model on new data sets and letting it perfect itself in complete autonomy.
This makes the machine learning model a program that has been trained on a database of learned data. Once developed, the model will generate results from data it has never processed before, providing a new predictive analysis. To give examples, this artificial intelligence will be able to translate a text based on speech recognition, identify a face in a photo, or suggest products and services based on search history.
What is the relationship between AI and machine learning?
The goal of artificial intelligence is to give a machine the ability to reason and behave like a human being. As a subset of AI, machine learning is a way to get closer to this goal. It can provide answers to new and complex situations since the machine learning system will adapt to different databases.
What are the different types of algorithms?
There are three types of AI algorithms for creating machine learning models:
- Supervised learning: the machine learns to classify labeled data, according to criteria previously determined by a human
- Unsupervised learning: the computer will classify raw data according to criteria it will determine by itself
- Reinforcement learning: the computer system will observe its environment and learn from its mistakes, testing different approaches to reach its goal.
In supervised learning, we find classification algorithms, linear regression, logistic regression, decision trees, or random forests.
In unsupervised learning, we use clustering, association, and dimensionality reduction algorithms.
What are the differences between deep learning and machine learning?
Deep learning is a branch of machine learning. This technology is inspired by the human brain system. It uses a deep neural network with the computational nodes resembling neurons and the network resembling the brain. It requires a large amount of data and computing power for training.
Used extensively in speech and visual recognition, deep learning is suitable for both supervised and unsupervised learning.
What is the value of machine learning?
With the rise of Big Data in the 2010s, artificial intelligence has taken a quantum leap forward. It has become essential to train machine learning models on large volumes of data. The main value of these complex systems lies in automating tasks and predicting trends that human analysis cannot detect.
What are the applications of machine learning?
Artificial intelligence, and especially machine learning, is now being deployed in all fields ranging from healthcare to finance.
Machine learning is best known to the general public for its voice assistants such as Apple’s Siri or Amazon’s Alexa. These technological marvels are based on natural language processing (NLP) in either text or voice data. It can be found in chatbots (written conversational robots) and callbots (spoken conversational robots). Machine learning AI systems are now widespread both in the corporate world and in everyday life.