Machine Learning is a subset of Artificial Intelligence. It focuses on using data to train computer systems and machines to identify patterns and make accurate predictions. Although they are used interchangeably, Machine Learning and Deep Learning work and learn differently.
Machine Learning algorithms analyse data, learn from it, and then make predictions. If a prediction is wrong, an engineer has to make corrections. Deep Learning is a subset of Machine Learning, which uses multiple layers of algorithms to create an artificial neural network. If functions very similarly to the human brain and can learn without being told what to do.
Some of the topics you can expect to study during a Bachelor's or Master's in Machine Learning are: statistical modelling, computer vision, speech technology, information retrieval, data visualisation and manipulation, machine learning fundamentals, autonomous sensing, reasoning and deep learning, etc.
Machine Learning specialists are in demand because companies from all industries are looking for new ways to analyse and use their data to improve efficiency, create better products, and increase profits. As a Machine Learning engineer, you can work on various kinds of applications. Some examples include tagging people and objects in photos, search engine recommendations, fraud detection, text and speech recognition, and others.
To become a Machine Learning professional, you need excellent data analysis and critical thinking skills, as well as advanced knowledge of mathematics, data science, and computer science. After graduation, Machine Learning students find work as Machine Learning engineers, data scientists, computational linguists, software engineers, etc.Read more