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Machine Learning is a subset of Artificial Intelligence to train machines to learn patterns and rules from data and apply them to provide business insights for better decision making. Machine Learning algorithms extract hidden patterns from data, otherwise undetected by human efforts, and use these patterns to predict outcomes in the future. Popular business use cases of Machine Learning involve Predictive analytics, recommender systems, price optimization & prediction, sales predictions, and fraud detection, etc. Machine Learning has found its use cases in almost industries like Finance, Health care, Retail & E-Commerce, education, manufacturing, life sciences, etc.

Predictive Analytics.


Predictive Analytics is a subset of Machine Learning and Artificial Intelligence which can be deemed as an Oracle to predict future, however, it predicts only after learning from historical data. Predictive analytics helps businesses act proactively, avoid risks and helps take the right decisions up ahead in advance.

Deep Learning.

Deep Learning is a set of advanced techniques in Machine Learning and AI, which are based on Artificial Neural Networks. A deep architecture of Neural Networks mimics how the human brain learns from experience. Although a compromise on the interpretability of models, deep learning techniques solve complex problems like image classification, detection, and segmentation, language translation and speech recognition, etc.

Natural Language Processing.

Natural Language Processing is a subfield of Artificial Intelligence focused on understanding human language and make it understandable to machines. NLP deals with text data and converts it to numbers to be comprehended by computers. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are two subfields of NLP. Text classification, sentiment analysis, speech-to-text, and text-to-speech are general use cases of NLP.

Computer Vision.

Computer Vision is a field to help machines process images and videos to extract information. Generally speaking, it helps computers see as humans do. Image Classification, image segmentation, image object detection, localization, image creation, image captioning and image style transfer are some of the many techniques used in computer vision. Mostly deep learning neural networks and convolutional neural networks are very popular tools for computer vision.