What Is Machine Learning?

In this article:

1. What is Machine Learning?
2. Machine Learning vs. Deep Learning vs. Neural Networks
3. How Does Machine Learning Work?
4. Machine Learning Methods
5. Common Machine Learning Algorithms
6. Real-world Machine Learning Use Cases

What is Machine Learning?

Machine Learning vs. Deep Learning vs. Neural Networks

How Does Machine Learning Work?

1. Decision Process: Machine learning algorithms are primarily utilized for prediction or classification purposes. By analysing input data, whether labelled or unlabelled, the algorithm generates estimations about patterns present within the data.
2. Error Function: An error function plays a crucial role in evaluating the accuracy of the model's predictions. When known examples are available, the error function compares the model's output to the expected values, enabling a comprehensive assessment.
3. Model Optimisation Process: To enhance the model's compatibility with the training set data, the algorithm adjusts the weights to minimize the disparity between the model's estimations and the known examples. This iterative "evaluate and optimize" process enables autonomous weight updates until a predefined accuracy threshold is attained.

Machine Learning Methods

1. Supervised Machine Learning: Supervised learning, also known as supervised machine learning, utilises labelled datasets to train algorithms and accurately classify data or predict outcomes. The model adjusts its weights as input data is provided, ensuring an appropriate fit. Cross-validation is employed to prevent overfitting or underfitting. Supervised learning solves real-world problems at scale, such as spam classification in email folders. Notable methods in supervised learning include neural networks, naïve Bayes, linear regression, logistic regression, random forest, and support vector machine (SVM).
2. Unsupervised Machine Learning: Unsupervised learning, or unsupervised machine learning, analyses and clusters unlabelled datasets using machine learning algorithms. These algorithms uncover hidden patterns and groupings without human intervention. Unsupervised learning is ideal for tasks like exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition. It also aids in dimensionality reduction by reducing the number of features in a model. Principal component analysis (PCA) and singular value decomposition (SVD) are common techniques. Other algorithms used in unsupervised learning include neural networks, k-means clustering, and probabilistic clustering methods.
3. Semi-Supervised Learning: Semi-supervised learning bridges the gap between supervised and unsupervised learning. It relies on a smaller labelled dataset during training to guide classification and feature extraction from a larger, unlabelled dataset. Semi-supervised learning is effective when labelled data is limited for a supervised learning algorithm or labelling enough data is costly.
4. Reinforcement Machine Learning: Reinforcement machine learning is a powerful model that shares similarities with supervised learning. However, it stands apart by eschewing sample data for training. Instead, it learns through a trial-and-error process, continuously improving by reinforcing successful outcomes and refining recommendations or policies for specific problems. A notable example of reinforcement learning is demonstrated by the IBM Watson® system, which achieved victory in the Jeopardy! challenge back in 2011. The system leveraged reinforcement learning techniques to determine optimal strategies, such as when to provide an answer or question, which board square to select, and how much to wager — especially when encountering daily doubles. This remarkable achievement highlights the practical application of reinforcement learning in complex decision-making scenarios. By combining machine learning with reinforcement techniques, organisations can unlock new opportunities for intelligent decision-making and problem-solving.

Common Machine Learning Algorithms

1. Neural Networks: Neural networks emulate the functioning of the human brain, comprising interconnected processing nodes. They excel at pattern recognition and play a crucial role in natural language translation, image recognition, speech recognition, and image generation.
2. Linear Regression: This algorithm predicts numerical values based on linear relationships between different variables. For instance, it can forecast house prices using historical area data.
3. Logistic Regression: As a supervised learning algorithm, logistic regression predicts categorical responses like "yes" or "no." It finds applications in spam classification and quality control on production lines.
4. Clustering: Using unsupervised learning, clustering algorithms identify patterns in data and group them accordingly. This assists data scientists in uncovering hidden differences between data points that may have been overlooked by humans.
5. Decision Trees: Decision trees are versatile algorithms that can predict numerical values (regression) and classify data into categories. They employ a branching sequence of linked decisions, visually represented as a tree diagram. Decision trees offer advantages such as easy validation and auditing, in contrast to the opaque nature of neural networks.
6. Random Forests: In random forests, the machine learning algorithm predicts values or categories by aggregating the results from multiple decision trees, leading to enhanced accuracy and robustness.

Real-world Machine Learning Use Cases

1. Speech Recognition: Also referred to as automatic speech recognition (ASR) or speech-to-text, this capability utilizes natural language processing (NLP) to convert spoken words into written text. Speech recognition is integrated into mobile devices, allowing users to conduct voice searches (e.g., Siri) or enhance accessibility for texting.
2. Customer Service: Online chatbots are transforming customer engagement across websites and social media platforms, replacing human agents. These chatbots answer FAQs, provide personalized advice, and assist with tasks like cross-selling products or suggesting sizes. They are commonly found on e-commerce sites, messaging platforms like Slack and Facebook Messenger, and virtual assistants.
3. Computer Vision: Computer vision, an AI technology, enables computers to extract meaningful information from images, videos, and visual data. Powered by convolutional neural networks, it has applications in social media photo tagging, healthcare radiology imaging, and autonomous vehicles.
4. Recommendation Engines: AI algorithms leverage past consumer behaviour data to identify patterns and develop effective cross-selling strategies. Online retailers use recommendation engines to suggest relevant products during the checkout process, enhancing the overall shopping experience.
5. Automated Stock Trading: AI-driven high-frequency trading platforms optimize stock portfolios by making thousands or even millions of trades per day autonomously, without human intervention.
6. Fraud Detection: Machine learning aids financial institutions in identifying fraudulent transactions. Supervised learning models are trained on known fraudulent activities, while anomaly detection algorithms flag atypical transactions for further investigation.