Before going deeper into ML and AI, it is good first to understand what big data is. In simple terms, big data refers to the vast amount of structured and unstructured data generated from different sources like social media, sensors, mobile devices, and more. Due to its massive volumes, big data poses significant challenges for businesses and organizations. These challenges include processing, storing, and analyzing the data to extract meaningful insights.
Artificial intelligence refers to computer systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. AI is often used in conjunction with machine learning to analyze big data.
Machine learning (ML) is a subset of AI that involves training computers to learn from data and make decisions without explicit programming or human intervention. The key advantage of this technology is that it can handle large amounts of data much faster than humans can. Moreover, ML algorithms can improve their accuracy over time by learning from new data gathered from various sources.
Supervised and unsupervised learning
Supervised learning involves training an ML model on labelled data, which has been tagged with a certain category or value. The model uses this data to learn how to predict the label of new data based on the labelled data. On the other hand, unsupervised learning entails training a ML model on unlabeled data, making the model identify patterns and similarities in the data without prior knowledge of the categories or values.
This is a subset of ML that uses artificial neural networks to learn from large and complex data sets. Deep learning models can process and analyze large amounts of unstructured data like images, text, and speech. Deep learning has two types which are convolutional neural networks (CNN) and recurrent neural networks (RNN), both of which are used in big data analytics.
CNNs are used for image and video processing because they are advanced and can learn to recognize features such as edges, shapes, and textures in images and videos. On the other hand, RNNs are used for sequential data processing since they are good in identifying patterns and dependencies in time-series data such as speech and language.
Natural Language Processing (NLP)
NLP is a branch of AI that enables computers to understand and process human language. In big data analytics, this type of AI is used in extract insights from unstructured text data like emails, social media posts, and customer feedback. Some NLP techniques such as sentiment analysis, topic modeling, and entity recognition are used in big data analytics.
Sentiment analysis involves analyzing the emotional tone of text data. On the other hand, topic modeling involves identifying the themes or topics discussed in large volumes of text data, while entity recognition entails the identification and categorization of entities like names, organizations, and locations in text data. Sentiment analysis can be used identify positive, negative, or neutral sentiments in customer feedback or social media posts. On the other hand, topic modeling can be used to identify the most popular topics discussed in social media or news articles. While entity recognition can be used to extract information from customer feedback or news articles.
With the continuing adoption of AI in various industries, it will continue to revolutionize big data analytics by enabling organizations to extract valuable insights from vast amounts of data. As the volume of data continues to grow, AI will become increasingly crucial in enabling businesses.