What Is Machine Learning?
In simple terms, machine learning (ML) is a subset of artificial intelligence (AI) that allows computers to learn without being explicitly programmed. Instead of following a set of rules, ML algorithms use data to identify patterns and make predictions. This data can be anything from text and images to sound and sensor readings. Machine learning algorithms are statistical models based on the foundations of mathematical optimisation methods.
ML approaches have been applied to many AI-related fields, including large language models, computer vision and speech recognition, among others.
What Are The Four Types Of Machine Learning Algorithms?
- Supervised Learning: In supervised learning, the machine is trained on labelled data, meaning each data point has a corresponding label or output value. The algorithm learns to map the input data to the desired output. Some of the common supervised learning tasks include classification (such as spam detection and image recognition) and regression (such as predicting house and stock prices).
- Semi-Supervised Learning: Semi-supervised learning uses a small amount of labelled data, along with a much larger amount of unlabelled data. The labelled data helps the machine learn a model, which is then used to label the unlabelled data. Some of the common semi-supervised learning tasks include image segmentation and text classification.
- Unsupervised Learning: In unsupervised learning, the machine is not given any labelled data. Instead, it must find patterns and structure in the data on its own. Some of the common unsupervised learning tasks include clustering (such as grouping customers into segments) and dimensionality reduction (such as reducing the number of features in a dataset).
- Reinforcement Learning: In reinforcement learning, the machine learns through trial and error. It interacts with an environment and receives rewards or punishments for its actions. The machine then tries to learn what actions lead to the most rewards. Some of the common reinforcement learning use cases include robotics, game playing and resource allocation.
What Are The Stages Of Machine Learning (ML)?
Machine learning (ML) projects typically involve a series of interconnected stages, each crucial for building and deploying a successful model. These steps are not always linear and developers may have to go through several iterations to address the challenges they face along the way to develop an ML model.
Problem Definition & Planning
- Identifying the problem: This step involves defining the task the machine learning model will solve. What are the goals and desired outcomes?
- Data exploration and feasibility: The available data, its quality and suitability for the chosen problem needs to be understood. Further, developers need to assess if collecting additional data is necessary.
- Performance metrics: Next, developers need to determine how they will measure the success of the machine learning model.
Data Preparation
- Data collection: The relevant data from various sources is gathered in this step, ensuring its accuracy and completeness.
- Data cleaning and preprocessing: The data is then preprocessed, including addressing missing values, outliers and inconsistencies in the data. This might involve formatting, normalisation and feature engineering.
- Data splitting: The preprocessed data is then divided into training, validation and testing sets for model training and evaluation.
Model Selection & Training
- Algorithm selection: An appropriate machine learning algorithm is selected in this step, based on the problem type, data characteristics and desired outcome.
- Hyperparameter tuning: The selected ML model’s internal parameters are then adjusted for optimal performance. This often involves experimentation and evaluation.
- Model training: The model is then trained on the training data, allowing it to learn the patterns and relationships within the data.
Model Evaluation
- Validation and testing: The model’s performance is evaluated on the validation and testing sets, assessing its accuracy, generalisability and robustness.
- Error analysis: The types of errors the model makes are investigated to identify potential areas for improvement.
- Model comparison: In certain cases, different models’ performances are compared to choose the best option.
Model Deployment & Monitoring
- Deployment: The chosen and trained model is integrated into the target environment, enabling it to make predictions on new data in real-time or batch mode.
- Monitoring and maintenance: The model’s performance is monitored continuously, tracking its accuracy over time, and identifying potential issues like data drift.
- Model retraining and improvement: The machine learning model is regularly retrained with new data or updated algorithms to maintain its performance and adapt to changing environments.
How Is Machine Learning Used In AI?
Machine learning is fundamentally connected with artificial intelligence (AI). Although it could be argued that ML forms the engine for many of AI’s intelligent capabilities, it is not synonymous with AI.
While ML provides learning capability, AI encompasses broader aspects like reasoning, planning and understanding the world. ML acts as a powerful tool that helps AI systems achieve intelligent behaviour.
ML algorithms are integrated into various AI systems to enable capabilities such as natural language processing (NLP), computer vision, recommendation systems and decision-making.
ML algorithms also enable AI systems to learn from data. Various core machine learning techniques, such as supervised learning, are used in AI models to allow them to learn from vast amounts of data.
What Are The Limitations Of Machine Learning?
- Data Dependence: ML algorithms heavily rely on the quality and quantity of data they are trained on. Insufficient or biased data can lead to inaccurate or unfair models. Further, gathering and labelling large amounts of data can be expensive and time-consuming.
- Interpretability and Explainability: Some complex ML models, like deep neural networks, can be difficult to interpret, making it challenging to understand how they arrive at their decisions.
- Generalisability and Overfitting: Models trained on specific data might not perform well on unseen data or in different contexts, leading to overfitting and poor generalisation.
- Computational Resources: Training complex models can require significant computational power and resources, which can be limiting for smaller organisations or applications.
- Limited Common Sense and Reasoning: Unlike humans, most ML models struggle to understand context and nuances in language or situations. This can lead to misinterpretations and errors.
