Machine learning is a technique of data analyses used by computer scientists and programming experts to automate analytical model building. It is a special area in artificial intelligence based on the idea that computers can identify data patterns, learn from them, and make appropriate decisions with minimal human assistance (Holzinger, 2016). This paper describes and evaluates machine learning processes and techniques used in modern computer systems and automated machines.

Labeled vs. unlabeled data sets

Labeled data comes with a label, while the counterpart does not. Supervised learning uses labeled data because it contains meaningful tags used modeling. On the other hand, the unlabeled dataset bears natural and human-created artifacts that can be used by unsupervised learning.

Supervised Machine Learning

Supervised learning is common in various applications in modern-day computing, including text processing, image recognition, recommendation systems, and several others. This kind of machine learning is characterized by using labeled datasets to train the algorithms and subsequently class data or make an accurate prediction of the outcomes. The input data is fed into the model, which then adjusts the data weights using a reinforced learning process to ascertain that the model is appropriately fitted. A vast majority of organizations use supervised machine learning to address real-world problems at a scale. For instance, the ability of an email application to classify a message as spam and put it in a different folder aside from the inbox is one capability of supervised machine learning (Holzinger, 2016). Generally, this learning language can be used in business organizations to eliminate the manual classification of work and predicting the future using the labeled data. Human expertise and intervention are needed to evade potentially overfitting data models when formatting the machine learning algorithms.

Supervised machine learning uses neural network tools that train the model by mimicking the interconnectivity of the human through layers of nodes. Every node has inputs, weights, thresholds, and output. According to Sidey-Gibbons et al. (2019), supervised machine learning uses a training set to teach models that eventually yield desired system functionality. The training dataset comes with inputs and correct outputs that the model has to learn for a specified period.

The language is also endowed with algorithms that measure its accuracy using the loss function, which adjusts until errors are minimized to zero levels.

Supervised learning can either be classification or regression, depending on the nature of data mining. Classification uses an algorithm to assign test data into specific categories (TensorFlow, 2019). This process identifies entities in a dataset and makes conclusions on how the model can label or define those entities. Some of the common classifications include decision trees, random forest, and support vector machines. On the other hand, regression defines the relationship between dependent and independent variables. The technique to estimate projections such as the sale revenues and commissions for the business. Popular regression models include polynomial regression, linear and logistical regressions.

Supervised machine learning used the Scitik-learn tool, whose development uses the Python programming language (TensorFlow, 2019). The tool is very useful in data mining and analysis. It also provides models and algorithms for classification and regression, which are the categories of supervised learning. The tool is easy to understand and learn, and most of its parameters are flexible to change for any algorithm while calling objects.

The rationale for Selecting an Analytic Tool

The choice of an analytic tool for machine learning depends on several factors that serve as the rationale for the ultimate decisions over-analytical tools. One of them is the business objectives in relation to the cost of acquiring the tool. User interface and visualization are often another crucial consideration. Scikit-learn as one of these tools is often preferred because of the ease of use and higher user interface. It also has advanced analytics that allows it to recognize data patterns and predict future trends and outcomes. It is also flexible in that it allows standalone solutions and the integration of other technological capabilities.

Machine Learning Process

The machine process is all about using tags and training the machine to learn those tags.

For instance, in training image recognition, the expert would need to tag photos of natural features such as lakes, rivers, forests, and mountains with appropriate names. This exercise is data labeling. When the user is working with machine learning text analysis, he/she would feed the text analysis model with text training data and then tag it, depending on the nature of the analysis being done (TensorFlow, 2019). For sentimental analysis, customer feedback, for instance, would be fed into the model and then train the model by tagging each comment as neutral, positive, or negative.

Generally, the machine learning process involves three steps. The first step involves feeding the machine learning training input. In this case, it could be customer reviews and feedback from customer service data and social media. The second step is tagging the training data with the desired output. For the case of the business-customer relationship, the sentimental analysis model would be told whether the customer review is positive, negative, or neutral. The model then transforms the training data into text vectors representing data features (Holzinger, (2016). The third step involves testing the modes by feeding it testing data. Accordingly, algorithms are trained to associate feature vectors with tags using the manually tagged samples and make predictions when handling and processing unseen data.

Uses of Machine Learning in Healthcare

Machine learning has several applications in healthcare and has been useful in meeting the growing needs of medical demands and improving operations at lower costs. For instance, at the bedside, machine learning innovation can assist healthcare practitioners in detecting and treating diseases more efficiently and with more precision and personalize care (Dai et al., 2015). Generally, this innovation has revealed how technology can yield holistic care strategies to improve the quality of care and subsequent patient outcomes.

One of the complex machine learning that mimics the functioning of the human brain is currently being used in radiology and medical imaging. Deep learning uses neural networks to detect, recognize and analyze cancerous lesions from images (Dai et al., 2015).

Machine learning in health informatics is also streamlining record-keeping through electronic health records. The use of artificial intelligence in EHR improves patient care, lowers healthcare and administrative costs, and optimizes healthcare operations.

Disease identification and diagnosis and medical imaging diagnosis are other areas of machine learning applications in healthcare practice. The machine learning algorithms can detect patterns associated with health conditions and diseases using information from thousands of healthcare records and existing patient data (Sidey-Gibbons et al., 2019).

Conclusion

Machine learning can either be supervised or unsupervised, and in most industry applications, supervised learning is preferred because it uses labeled data to make ideal predicting future events and outcomes. Additionally, supervised learning can be classification or regression whose difference is the nature of the output. Machine learning has been applied in many areas to improve the efficiency and speed of operations while lower errors and costs. In healthcare, machine learning has been used in medical and imaging diagnoses, treatment interventions, and healthcare records, and data management.