Basic Terms of Artificial Intelligence

AI : It aims to make computers achieve human-style intelligence. ML and DL are subsets of AI.

πŸ“Œ Machine Learning

 Data Mining β€” This is a process in which we gather data and pre-process it. For data mining, we can do web scraping/crawling. Through data mining, we can turn raw data into useful information for machine learning applications.

βœ… Predictive Modelling β€” It is a process that uses data and statistics to predict outcome with data models. These models can be used to predict anything like car selling price prediction, rainfall prediction, some score prediction, etc.

βœ… Regression β€” It consists of a set of machine learning methods like linear regression, decision tree regression, support vector regression, etc. These models are used to predict continuous value like house price prediction, website visitor prediction, CPU memory usage prediction, etc.

βœ… Classification β€” It consists of a set of machine learning methods like logistic regression, decision tree classification, naive Bayes, random forest classifier, etc. These models are used to predict some categorical output like gender classification, weather classification(sunny, rainy, cloudy, etc.), brain tumor classification, etc.

βœ… NLP β€” It involves a set of techniques for identifying parts of speech, entities, sentiment, and other aspects of the text. Typical NLP tasks are information retrieval, information extraction, language generation, text clustering, text classification, machine translation, grammar checkers, etc.

πŸ“Œ Computer Vision

βœ… Image Transformations β€” In deep learning there is an algorithm called GAN(Generative Adversarial Networks). Which is having many variants based on requirements. It is widely used nowadays for image transformations. Applications involve image generation, text to image, segmentation mask to the image, low-resolution image to super-resolution, etc.

βœ… Image Classification β€” For image classification, we can use traditional machine learning algorithms like SVM, logistic regression, etc. But deep learning is giving very good results on Image applications. Specially CNNs(Convolutional Neural Networks). As the name says it is used for image classification purposes.

βœ… Object Detection β€” In object detection, we localize the object in an image. Also if we want to count a number of objects we use object detection.

βœ… Image Recognition β€” It is a task in which we recognize different objects from an image. It is a combination of image classification and object detection. A very useful application is face recognition.

βœ… Image to Text β€” It is a combination of computer vision and NLP.
(For all the Points above, there are multiple states of the art architectures(methods/models) to do each task). Also, we can define our own model.

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