In last week’s blog we talked a little bit about AI and briefly mentioned machine learning as a part of it. And as we hinted about it in the last blog update, we are going to be discussing machine learning this week. So let’s dive in!
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. It has enhanced many industrial and professional processes as well as our daily lives. It’s a subset of artificial intelligence (AI), which focuses on using statistical techniques to build intelligent computer systems to learn from available databases.
So how does machine learning work and what is it used for?
Machine-learning algorithms use statistics to find patterns in massive amounts of data. Data encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.
Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on. Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalized recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.
The 4 types of machine learning are:
- Supervised Learning. Supervised learning is applicable when a machine has sample data, i.e., input as well as output data with correct labels.
- Unsupervised Learning. This king of machine learning uses algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.
- Reinforcement Learning. This area of machine learning is concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
- Semi-supervised Learning. This is a combination of supervised and unsupervised learning. It uses a small amount of labeled data and a large amount of unlabeled data, which provides the benefits of both unsupervised and supervised learning while avoiding the challenges of finding a large amount of labeled data.
Now that we covered some of the basics, let’s go over the learning system of a machine learning algorithm. These can be divided into three main parts.
- A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
- An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
- A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
A number of machine learning algorithms are commonly used. These include:
- Neural networks: Neural networks simulate the way the human brain works, with a huge number of linked processing nodes. Neural networks are good at recognizing patterns and play an important role in applications including natural language translation, image recognition, speech recognition, and image creation.
- Linear regression: This algorithm is used to predict numerical values, based on a linear relationship between different values. For example, the technique could be used to predict house prices based on historical data for the area.
- Logistic regression: This supervised learning algorithm makes predictions for categorical response variables, such as“yes/no” answers to questions. It can be used for applications such as classifying spam and quality control on a production line.
- Clustering: Using unsupervised learning, clustering algorithms can identify patterns in data so that it can be grouped. Computers can help data scientists by identifying differences between data items that humans have overlooked.
- Decision trees: Decision trees can be used for both predicting numerical values (regression) and classifying data into categories. Decision trees use a branching sequence of linked decisions that can be represented with a tree diagram. One of the advantages of decision trees is that they are easy to validate and audit, unlike the black box of the neural network.
- Random forests: In a random forest, the machine learning algorithm predicts a value or category by combining the results from a number of decision trees.
Where can we find machine learning being used? – Well it’s quite common in: speech recognition, customer service, computer vision, fraud detection, automated stock trading, recommendation engines and more. AI and machine learning are all around us and make our lives easier every day!