Recent technology advancements have markedly enhanced the capacity to generate, store, and process data. The automation of transactions across multiple sectors (commercial, business, economic, and scientific ) alongside swift access to information via the Internet has contributed to this data proliferation. Infrastructure has improved, and now firms can store vast amounts of data, such as customer, patient or market data. Traditional data management methods struggled to cope with the vast volumes, and the complexities of relationships between different data pieces began to outpace those that human analysts could handle. Meanwhile, database systems continued to extend their original ragged edges, leading many in the organisation, both within IT and not, to fear inefficiency, reduced responsiveness to business needs, or compromised faith in decision making. Hence, Big Data Mining has emerged as a crucial research field, where we can discover important patterns, insights and predictions from large-scale data. ML plays a vital role in achieving this by providing scalable and efficient algorithms to deal with big data under the considerations of volume, velocity and variety. This course provides an overview of essential ML algorithms employed in Big Data Mining, classified into supervised, unsupervised, and reinforcement learning methodologies.
Introduction to Machine Learning
📆semester2-السداسي2