Through the course of this blog, we will understand what is machine learning and the process of machine learning. 

The 21st century is the age of Artificial Intelligence. According to renowned computer scientist Tom Mitchell, the definition of AI is as follows, “Machine learning is the study of computer algorithms that allow computer programs to improve through experience automatically”. This experience is based on past historical data. This is the age of data, where data is not only consumed, instead created or produced by users and machines. Earlier data was generated only by humans in textual form, but now data is generated by computers, mobile phones, automated machines in massive amounts in real-time. This data is available in text, pictures, music, video, spreadsheets or a combination of these. Earlier, humans perform the task of analyzing the data. But as the size of the generated data becomes very much higher, and the type also varies, it becomes difficult for humans to analyze the data manually and derive meaning from the available data. A new term Big Data is used now for this available data due to its variety, size, speed of generation (velocity), veracity (quality or integrity of data) and value (Usefulness of data). It becomes very complex to identify the patterns and trends in the data with the rise of size in data manually in real-time. As such, companies rely on high-quality data valuation to understand the potentials of their businesses.

So specialized methods and tools are required to perform this task.  So Machine Learning (ML) tools and techniques are required to automate this analysis and prediction tasks. Machine learning is part of artificial intelligence in the computer science and engineering area. Machine learning is the set of tools and techniques that helps derive the meaning from the available data and make decisions based on derived meaning.  So Machine learning is a set of data-driven techniques which use historical data or training data to identify the patterns or to perform predictions for the unseen data (test data).  Machine learning is used for developing advanced technology systems like search engines for information recovery, in the medical field, for identifying the disease from the image, self-driving cars, image recognition, computer vision, recommender systems, personal digital assistants (Google digital assistant, Cortana, Siri, Alexa), trend prediction, and forecasting fields.

The machine learning process contains the data selection, data pre-processing, model making, model training, validation or fine-tuning of the model, predictions based on unseen data (test data) and accurate measurement of the ML model. First, identify the right source of data and collect data from there and then pre-process the data to remove anomaly and abnormality in the data. Then divide the data into three parts one is known as training dataset, second is known as validation data set and third is known as test data set after this ML model is created, which is the set of mathematical equations that comprises various parameters, constants, and input and output variables. The next phase is model training with training data set in. This ML model learns various patterns and trends hidden in the training data set and automatically adjusts its model parameters accordingly. As the amount of data increases, the machine learning model learns the more diverse pattern hidden in data. During this phase, model learning is required.  

Machine learning has three types of the learning process. First is supervised learning, second is unsupervised learning, third is reinforcement learning. The supervised has labelled data and a mapping function. The labelled data have both input value and corresponding output value, for example, fruit images with related fruit names. The model learns from the input data and predicts as per expected output. When new data or unseen data (test data) is used as input, it predicts the output correctly. Some supervised machine learning techniques are known as linear regression, random forest, and support vector machines. Unsupervised learning has non-labelled data that is data have input values but no corresponding output values. So the unsupervised method identifies the patterns based on intrinsic characteristics of the available data, and based on that, it creates groups or clusters of training data. Once a model learns from these clusters, it predicts output for unseen data (test data). One example of this is the clustering process. The output has no correct answer, and the only model identifies the available pattern in the data. The third type of machine learning is reinforcement learning, based on two things known as environment and agent. When the agent takes the correct actions, a reward is given to the agent while penalized for the wrong action. Based on the rewards, the agent improves its environment to take appropriate action. After performing the ML model, the training accuracy is calculated for the prepared ML model  known as training accuracy.

The next step is model validation or fine-tuning of ML data. This prepared model is used to perform predictions using a validation data set. If the model validation accuracy score is low, then the model is not fine-tuned, means training data contains noise that is undesirable data or missing data. To increase the ML model’s accuracy, pre-processing of the training data is required, and model parameters readjustments are needed. It is achieved by multiple training rounds with training data in batches. 

The last phase of the ML process is predicting unseen data (Test data) and measurement of test accuracy score. In this phase, predictions are made using the test data set, and the test data set accuracy score is calculated. That is how much percentage the model predicts right for the test data set.  The accuracy of prediction or machine learning model is the numerical score that tells about the correctness of the prediction done by the model. Higher is the prediction accuracy of the model for a particular ML problem, more useful is the model. Next, graphical tools are used to represent the meaning derived from the data and take decisions based on that. 

Machine learning is used in various fields by using data. A mathematical model is created that learns from the available data and answers the questions based on the data. Hope this article helps you a lot for understanding this topic. If you’re interested in free online courses with certificates, So enroll today on Great Learning Programme. Happy learning!