Machine Learning
What is it?
Machine learning, or ML, is an artificial intelligence (AI) subset that empowers software applications to improve the accuracy of outcome prediction even if they are not explicitly designed for this purpose. ML entails the development and utilization of algorithms that learn patterns from historical data and thus serve to make predictions on new data.
Types of ML
Basic types of machine learning include the following:
- Supervised (AI gets labeled training data and assesses defined variables for correlations)
- Unsupervised (scanning through unlabeled data and searching for meaningful connections)
- Semi-supervised (algorithms explore a little labeled data and plenty of unlabeled data)
- Reinforcement (the machine learns to complete a multi-stage process with specified rules)
How does it work?
In terms of supervised machine learning, it is necessary to train algorithms by using labeled inputs and desired outputs. The tasks involved are splitting data into two categories (binary classification), selecting between more than two kinds of answers (multiclass classification), predicting continuous values (regression modeling), and combining predictions of many ML models to make a precise prediction (ensembling).
Unsupervised algorithms are employed, for example, in most deep learning types, which leverage artificial neural networks simulating the human brain. This kind of machine learning focuses on tasks like diving the dataset into similarity-based groups (clustering), detecting uncommon data points in the dataset (anomaly detection), recognizing frequently co-occurring items (association mining), and decreasing the number of variables (dimensionality reduction).
At the same time, semi-supervised learning requires an algorithm to learn dimensions from a small amount of labeled data and apply them to unlabeled data. Algorithms are taught to perform machine translation, fraud detection, and data labeling, i.e., applying labels to large data sets after training on small ones.
Lastly, reinforcement learning is about training algorithms for a predetermined goal. Additionally, artificial intelligence learns to aim for positive rewards and evade punishment. This ML type is adopted in robotics, video gameplay, and resource management.
Where is it used?
Typical applications of machine learning encompass recommendation engines (e.g., one that powers Facebook feed), customer relationship management software, business intelligence and analytics, human resource information systems, self-driving vehicles, and virtual assistants.
Application example
For instance, machine learning helps Facebook to customize feed delivery to each user. If a person tends to prefer some posts published on this social network in a specific group, this group’s posts and its other activity will start appearing higher in the feed.
Challenges
Firstly, machine learning projects involve high expenses for data scientists’ work and corresponding infrastructure. The second challenge is machine learning bias, such as training on data sets that leave out certain populations and build an inaccurate or even discriminatory understanding of the world.
