Machine Learning is a subfield of artificial intelligence focused on developing algorithms and models that allow computers to learn and make decisions or predictions without being explicitly programmed.

These algorithms and models can analyze and learn from large amounts of data and identify patterns, relationships and underlying structures to extract insights and knowledge.

They can improve their performance with time from new data in a sequential manner rather than undergoing only a one-time training process from a fixed dataset.

Using this learnt knowledge, they can make predictions, classify data, and perform well in dynamic environments, while continually learning, adapting, and updating themselves.

They learn from historical data to automate decision-making processes based on past experiences and decisions.

Machine learning algorithms use mathematical and statistical techniques such as linear regression, decision trees, random forest, neural networks, and other deep learning architectures to learn from data and make predictions or decisions.

ML algorithms and models undergo a “training” or “learning” process where the following occurs:

Data Collection:

• Data representing the problem or task is collected to train the AI system to solve it.
• The collected data involves inputs and their corresponding outputs.

Data Preprocessing:

• The data undergoes preprocessing to ensure its quality and suitability to train the AI system.
• Preprocessing involves handling missing values, converting numerical values into a scale or range, and identifying and managing data inputs that vary considerably from the others.
• It ensures that the data fed is consistent, usable and not negatively affecting the training process.

Algorithm Selection:

• Based on the problem or task as well as the type of data collected, a suitable machine learning algorithm or model is selected.
• Some popular machine learning algorithms or models are linear regression, logistic regression, decision trees, random forest, neural networks, and support vector machines.
• More advanced machine learning models like Convolutional neural networks (CNNs) or Recurrent neural networks (RNNs) are also available.

Feature Engineering:

• It involves selecting and transforming inputs to enhance the training and performance of the ML model using feature selection, dimensionality reduction, or even creating new features depending on the problem or task the model is training to solve.

Training the Model:

• We present the machine learning model with labelled training data allowing it to identify and learn patterns, relationships or dependencies between the inputs and outputs.

Loss function and optimization:

• A “Loss function” measures the difference between the output by the model and the predicted output.
• The AI model’s objective is to minimize this loss function by adjusting its internal parameters and finding the optimal values with the help of optimizing algorithms such as gradient descent.

Model Evaluation:

• After training, we evaluate the model’s performance with a separate dataset.
• We assess the model’s performance on precision, accuracy, recall (how accurately it identified positive cases in the data) and an F1 score (a harmonic mean of precision and recall).

Hyperparameter tuning:

• Machine learning models often have “hyperparameters” that control behaviour, such as learning rate, regularization rate (the ability of a model to generalize unseen data), or the number of layers present in a neural network.
• They are tweaked and tuned to find the best possible configuration for a given problem or task using techniques like grid search, random search, or even an advanced method like Bayesian optimization.

Deployment and Prediction:

• After training and evaluation, we deploy the model to make predictions on new unseen data.

The overall performance and accuracy of the ML model depend on factors such as the quality of training data, selected algorithm, feature engineering, and hyperparameter tuning performed.

Industries, domains and services, including healthcare, finance, marketing, and recommendation systems, use machine learning algorithms for their decision-making ability, insight extraction from data, and ability to adapt to dynamic environments making it a powerful tool for solving complex problems and advancing artificial intelligence.