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Machine Learning

Building machine learning models that can generalise well on future data necessitates careful analysis of the data at hand as well as assumptions regarding the different available training algorithms. The ultimate evaluation of the consistency of a machine learning model necessitates the required selection and analysis of measurement parameters. <br>More details please visit our site : https://ivasystems.in/model-building-and-algorithms-develoment-services-in-technopark/

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Machine Learning

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  1. MACHINE LEARNING

  2. MACHINE LEARNING Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Terms used in the contex of machine learning Data Mining Features Labels Models Accuracy and percision 5. 1. 2. 3. 4.

  3. Data Mining Data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems Features In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Labels In machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.

  4. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Accuracy and percision In pattern recognition, information retrieval and classification (machine learning), precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance.

  5. Machine Learning Techniques Machine learning can be classified into 3 types algorithms. 1. 2. 3. 4. Supervised Learning Unsupervised Learning Reinforcement Learning

  6. Advantages of Machine Learning Continuous Improvement. Machine Learning algorithms are capable of learning from the data we provide Automation for everything Trends and patterns identification Wide range of applications Data Acquisition Highly error-prone Algorithm Selection Time-consuming

  7. THANK YOU

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