Introduction to Machine Learning

Machine learning uses data and algorithms to imitate how humans learn, gradually improving its accuracy. It is a branch of artificial intelligence (AI) and computer science. Over the last few decades, technological advances in storage and processing power have enabled innovative products based on machine learning. As a result, machine learning is an essential component of the emerging field of data science. Using statistical methods, algorithms are created to make classifications or predictions and uncover critical insights in data mining projects.

These insights drive decision-making within applications and businesses, impacting key growth metrics. With the expansion and growth of big data, the market demand for data scientists will also increase. Data Scientists will be needed to pick the most relevant business problems and the data required to solve them. Machine learning algorithms are usually created using frameworks that accelerate solution development.

There are three significant categories of Machine learning models: supervised machine learning, unsupervised machine learning and semi-supervised learning.

Supervised machine learning deals with using labeled datasets to train algorithms to correctly categorize data or predict outcomes. This model helps organizations offer solutions to various real-world problems at scale. Some methods used in supervised learning are naïve bayes, linear regression, support vector machine (SVM), logistic regression, neural networks and random forest.

Unsupervised machine learning utilizes machine learning algorithms to inspect and gather unlabeled datasets. These algorithms reveal hidden patterns or data groupings without human intervention. Furthermore, it is utilized to reduce the number of features in a model through dimensionality reduction. A few algorithms utilized in this learning include k-means clustering, probabilistic clustering methods and neural networks.

Semi-supervised learning is beneficial because it provides a medium between supervised and unsupervised learning models. This learning can solve the problem of insufficient labeled data for a supervised learning algorithm. It also helps if it’s too costly to label enough data.

However, machine learning is not free from limitations like every technology available. Its demerits include explainability and biasedness.

Explainability refers to understanding what the models are doing and how they arrive at a decision. It is important to first understand the process and factors undertaken by machines to make conclusions and then receive and use the decisions accordingly. There is a concern that systems can be fooled and undermined.

Another concern is biasedness.Humans train machines. Hence human biases can be reflected in algorithms — if this biasedness is transferred to a machine learning program, the program will automatically learn to replicate it and perpetuate forms of discrimination.

Even with these concerns, outstanding machine learning technology has a broad scope and is advancing to offer better accuracy and efficiency.