AI & Machine
Object detection, face detection, handwriting recognition, and other similar tasks are all examples of applications that computer vision can perform
Image processing includes rescaling the image (also known as digital zoom), correcting the illumination, and changing the tones, sharpening.
Generative AI can be used to generate human-like text, including creative writing, storytelling, and even chatbots. It can be used to augment existing datasets by generating new samples to train machine learning models effectively.
NLP has diverse applications across industries, including sentiment analysis, text classification, machine translation, question-answering systems, speech recognition, and text generation.
Azure, Microsoft’s cloud platform, offers an extensive suite of cloud services such as computing, storage, databases, AI, analytics, and IoT, enabling versatile application development and scalable solutions for businesses.
Google is a global technology company that provides search, advertising, cloud computing, and software services, as well as developing hardware and operating systems. It’s known for innovation and internet dominance.
AWS features and tools support various aspects of DevOps and MLOps, including version control, continuous integration, continuous delivery, infrastructure automation, monitoring, and machine learning workflow management.
Data preprocessing is a fundamental step in the data analysis and machine learning pipeline. It involves the transformation and preparation of raw data into a format suitable for analysis and modelling.
Data analysis is a systematic process of inspecting, cleaning, transforming, and modelling data to discover meaningful patterns, extract insights, and support decision-making. Data analysis information that can drive informed actions and strategies.
Feature extraction helps to reduce the amount of redundant data from the data set. In the end, the reduction of the data helps to build the model with less machine effort and also increases the speed of learning
Through data visualization, insights and patterns in data can be easily interpreted and communicated to a wider audience, making it a critical component of machine learning.
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