Machine Learning

How does machine learning work?

Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions based on that data. Here’s a high-level overview of how machine learning works:

Data Collection: The process begins with the collection of relevant and representative data. This data can come from various sources, including sensors, databases, text, images, and more. The quality and quantity of data are crucial for the success of a machine learning model.
Data Preprocessing: Raw data is often noisy and may contain errors. Data preprocessing involves tasks like cleaning, normalisation, and feature engineering. This step aims to prepare the data for training and analysis.
Data Split: The data is typically divided into two or more sets: a training set, a validation set, and a test set. The training set is used to train the model, the validation set helps in tuning hyperparameters, and the test set is used to evaluate the model’s performance.
Feature Extraction: In many cases, not all the raw data is directly useful for the model. Feature extraction involves selecting or transforming the relevant attributes or features that will be used as input to the machine learning model.
Model Selection: Machine learning algorithms or models come in various types, such as decision trees, neural networks, support vector machines, and more. The choice of the model depends on the problem you’re trying to solve and the characteristics of the data.
Model Training: The selected model is trained using the training data. During training, the model learns to recognize patterns and relationships in the data. The algorithm adjusts its internal parameters to minimize the difference between its predictions and the actual outcomes in the training data.
Model Evaluation: After training, the model’s performance is assessed using the validation set. This step helps in fine-tuning the model and optimising its hyperparameters. The goal is to ensure that the model generalises well to new, unseen data.
Model Testing: Once the model is fine-tuned, it’s tested on the separate test dataset to evaluate its performance in a real-world scenario. This step provides a final assessment of the model’s capabilities.
Deployment: If the model meets the desired performance criteria, it can be deployed in a real-world application, where it can make predictions or decisions based on new, incoming data.
Continuous Learning: Machine learning models can be set up for continuous learning by periodically retraining them with new data. This allows the model to adapt to changing patterns in the data and remain effective over time.

Types of Machine Learning

Supervised machine learning

Supervised machine learning is a type of ML that involves the use of labeled datasets for training. In this approach, the machine learns to predict outputs based on the provided training data, where the dataset specifies known input-output pairs. During the training phase, the machine is exposed to inputs and their corresponding outputs, allowing it to learn the relationship between the two.
For example, imagine a dataset containing images of both parrots and crows. In the training phase, the machine is taught to recognize distinctive features such as color, eyes, shape, and size associated with each bird type. After training, when presented with a new image of a parrot, the machine leverages its acquired knowledge to identify the object and make a prediction. It does so by analyzing the color, eyes, shape, and other relevant features in the input image. This process exemplifies the supervised machine learning approach for object identification, where the machine’s ability to generalize from labeled data enables it to make accurate predictions on unseen inputs.

Unsupervised machine learning

Unsupervised learning is a machine learning technique that operates without the guidance of labelled data or supervision. In this approach, the machine learns from an unlabeled dataset and autonomously extracts patterns, similarities, and differences within the input data.
For example, consider an input dataset comprising images of a mixed assortment of fruits within a container. These images lack pre-assigned labels or categories. When this dataset is presented to an unsupervised learning model, the model’s objective is to discern underlying patterns in the images, which could include characteristics such as color, shape, or variations among the objects. Through this process, the machine groups similar images together, creating clusters or categories. Subsequently, when tested with new data, the machine applies the acquired categorization to predict the output, demonstrating the power of unsupervised learning in uncovering inherent structures and relationships within unannotated data.

Semi-supervised learning

Semi-supervised learning bridges the worlds of both supervised and unsupervised machine learning, harnessing the power of both labeled and unlabeled datasets to train its algorithms. This approach offers a compelling solution that combines the advantages of the aforementioned techniques.
To illustrate this concept, think of a college student’s learning process. In supervised learning, a student acquires knowledge with the direct guidance of a teacher in a classroom setting. Conversely, unsupervised learning involves a student independently learning a concept at home, without any external guidance.
Semi-supervised learning can be likened to a student who initially learns a concept in a structured classroom environment, guided by a teacher (akin to supervised learning). However, the learning doesn’t stop there. The student continues to refine and expand their understanding of the subject independently, even after the formal teaching has ended. This semi-supervised approach combines the benefits of both guided and self-directed learning, allowing the student to build a more comprehensive and nuanced understanding of the topic. In the realm of machine learning, this hybrid approach leverages the strengths of labelled data (supervised) and the capacity to uncover hidden patterns in unlabeled data (unsupervised) to enhance model performance and adaptability.

Reinforcement learning

Reinforcement learning is a dynamic and feedback-driven process. In this approach, the AI agent actively interacts with its environment through a trial-and-error method, taking actions, learning from its experiences, and striving to enhance its performance. The core principle is simple: the agent is incentivized with rewards for favourable actions and incurs penalties for detrimental decisions. Consequently, the reinforcement learning component is geared toward maximising its cumulative rewards by making optimal choices over time.
In contrast to supervised learning, reinforcement learning operates without the luxury of pre-labeled data. Instead, agents embark on a journey of experiential learning. Picture this in the context of video games: the game itself serves as the environment, with each move the reinforcement agent makes defining its current state. The agent receives real-time feedback in the form of rewards and punishments, influencing the overall game score. The ultimate objective for the agent is to master the game, achieving the highest possible score by learning from its interactions and progressively fine-tuning its decision-making process. This process captures the essence of reinforcement learning, where autonomous agents navigate a complex space to optimise their performance based on the outcomes of their actions.

Machine Learning Applications

Healthcare industry:

The healthcare industry is experiencing a significant uptick in the adoption of machine learning, thanks in large part to the proliferation of wearable devices and sensors like fitness trackers and smart health watches. These devices continuously monitor users’ health data, providing real-time insights into their well-being.
Furthermore, this technology is proving invaluable for healthcare professionals. It empowers them to not only analyze trends in patients’ health data but also to identify
critical events that can lead to more accurate diagnoses and more effective treatment strategies. Machine learning algorithms, in particular, have the capacity to forecast the lifespan of patients grappling with life-threatening illnesses with an ever-increasing level of precision. This marks a notable advancement in the field, as it enables healthcare providers to make more informed and personalized decisions, ultimately leading to enhanced patient care.

Finance sector:

In the contemporary financial landscape, numerous financial institutions and banks have embraced machine learning technology as a potent weapon against fraudulent activities and as a means to extract critical insights from the enormous troves of data at their disposal. These machine learning-driven insights play a pivotal role in pinpointing promising investment opportunities, equipping investors with the intelligence needed to make well-informed trading decisions.
Furthermore, machine learning is a linchpin for data mining techniques employed in cyber-surveillance systems. These systems serve as vigilant sentinels, meticulously scanning for telltale signs of fraudulent activities and promptly taking countermeasures to thwart them. This proactive approach enables financial institutions to stay ahead of potential threats and maintain the integrity of their operations.

Travel industry:

Machine learning is driving a remarkable transformation in the travel industry, with companies like Uber, Ola, and advancements in self-driving cars showcasing its significant impact. These platforms rely on robust machine learning backends to enhance user experiences.
For instance, let’s take a closer look at Uber’s machine learning algorithm, which underpins the dynamic pricing of their rides. Uber leverages a sophisticated model known as ‘Geosurge’ to manage the intricacies of dynamic pricing. This model draws from real-time predictive analytics, considering factors such as traffic patterns, supply, and demand. Here’s where it gets interesting: imagine you’re running late for an important meeting and need to book an Uber in a congested area. This is precisely when the dynamic pricing model springs into action. While you may secure an Uber ride immediately, the fare is adjusted to reflect the increased demand, often resulting in a higher cost, ensuring a quicker response and a reliable service, even in peak hours. This exemplifies how machine learning is reshaping the way transportation services operate, optimizing the user experience while adapting to changing conditions in real-time.

Social media:

Machine learning stands as a pivotal force in the realm of social media, orchestrating the seamless engagement of billions of users. Its impact extends across various dimensions, from tailoring personalized news feeds to delivering targeted advertisements.
Consider Facebook, for instance, which employs image recognition powered by machine learning to introduce an auto-tagging feature. This ingenious technology identifies familiar faces within your network, automatically tagging friends with remarkable precision. Facebook’s employment of artificial neural networks (ANN) enables it to recognize these familiar faces in your contact list, streamlining the process of tagging and enhancing the overall user experience.
In a similar vein, LinkedIn harnesses the capabilities of machine learning to offer a suite of features that guide users in their professional journeys. From suggesting opportune moments to apply for a new role to recommending meaningful connections and even gauging the competitiveness of your skills in comparison to peers, machine learning is the driving force behind these invaluable functionalities. The result is a more tailored, insightful, and engaging social media experience that empowers users to navigate their personal and professional networks with greater efficiency and efficacy.