Machine Learning is exactly what it sounds like, a “machine learns” its way around the provided data and makes informed decisions based on it. This concept emerged when Aurther Samual, who is a pioneer in AI and coined the term “machine learning”, published a paper in 1959 in the journal Research and Development. He conducted a study involving a computer programmed to master the game of checkers more proficient than a human player. The computer was equipped solely with the game rules and a strategic framework, enabling it to enhance its gameplay through self-learning processes.
According to that article, the machine took a lot less time than a human requires to play the game strategically. (A.L.Samuel, July 1959). This was the introduction of machine learning to the world.
Machine learning can be defined as a branch of Artificial Intelligence that involves the development of programs and algorithms that allow computers to access data and learn based on the trends emerging from it.
Simply put, machine learning is the development of algorithms that can learn from the data, analyze it, and make predictions on it. These algorithms are fed training or sample data to make predictions without explicitly programming them to do so. This leads to the automation of many processes and an increase in the efficiency of a wide variety of tasks.
For example, consider the chatbots we encounter daily. Without machine learning, managing user queries manually would be labour-intensive and time-consuming. However, machine learning streamlines this process, saving costs and time while enhancing overall efficiency.
While machine learning may sound like a concept straight out of a fantasy or science fiction novel and despite the notion of computers making autonomous decisions feeling like a scene from a movie, the reality is that it’s widely used across various industries and businesses and machine learning algorithms are actively employed to analyze data, extract insights, and facilitate decision-making processes in real-world scenarios.
Most industries who are working with large amounts of data are incorporating machine learning to make informed decisions, reduce risks and errors and get an advantage in the market.
- Government
- Transportation
- Health care
- Retail
- Financial industry
Machine learning isn’t just confined to large-scale applications; it’s also revolutionizing our day-to-day lives in various small-scale scenarios. From self-driving cars to email spam filtering, from image and speech recognition to virtual assistants like Siri or Alexa, and even the autocomplete feature while typing – machine learning is seamlessly integrated into numerous aspects of our daily routines.
These technologies have become indispensable tools, enhancing efficiency and productivity while minimizing errors.
What are these machine learning algorithms?
An algorithm is a set of rules adhered to in a process or a problem-solving operation. Algorithms for machine learning have been divided into four types.
- Supervised Learning
The supervised learning algorithm learns by examining a defined set of data i.e. input with correct and targeted outputs.
A real-world example of this model is spam filtering of emails. The algorithm examines the text content of emails labeled as spam and non-spam and decides based on that whether an incoming email is a spam or not.
- Unsupervised Learning
The provision of unlabeled data with unknown properties is unsupervised learning. This makes it time-consuming and costly, but they are more useful for clustering and association problems.
An e-commerce platform wants to group customers based on their purchasing history. The dataset uses customer’s details like transaction data, demographics, and browsing behavior. This dataset, however, is unlabeled as it does not indicate customer segments. The algorithm will automatically identify patterns and clusters in the data.
- Semi-supervised Learning
Combination of labeled and unlabeled data to improve the performance of algorithms is semi-supervised machine learning. A smaller quantity of labeled data and larger pool of unlabeled data is used, and pseudo labeling is employed to enhance the learning of algorithms.
Consider automatic image classification as an example. The dataset includes different images but a limited amount of it is labeled. The remaining images are unlabeled. Through predictive modeling, the unlabeled dataset is classified using pseudo labels. This iterative process is then repeated, gradually improving the algorithm’s ability to accurately classify images by learning from the labeled and pseudo-labeled data.
- Reinforcement Learning
A trial-and-error approach is employed to train the reinforcement learning algorithm. A reward-and-punishment strategy is used where the algorithms learn by receiving feedback on each action. The system adapts to human expertise and preferences to make predictions.
A robot can be trained to perform specific tasks using a reinforcement algorithm. How it responds to different queries, situations, problems, and other circumstances by simulating the preferred response. This enables the robot to develop adaptive and contextually appropriate responses to diverse scenarios, enhancing its capabilities to fulfil its assigned tasks effectively.
Citation
L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” in IBM Journal of Research and Development, vol. 3, no. 3, pp. 210-229, July 1959, doi: 10.1147/rd.33.0210.
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