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From Novice to Expert: A Journey into Training Machine Learning Models

Authors: I.V. Dwaraka Srihith; A. David Donald; T. Aditya Sai Srinivas; G. Thippanna; P. Vijaya Lakshmi;

From Novice to Expert: A Journey into Training Machine Learning Models

Abstract

Machine learning has evolved into a priceless asset for tackling complex obstacles across a wide range of disciplines, including Computer Vision(CV), Natural Language Processing(NLP), healthcare, and finance. At the core of machine learning lies the training process, wherein model parameters are optimized to make precise predictions on unseen data. For beginners venturing into this domain, it is crucial to grasp the fundamentals of training machine learning models. This article serves as a comprehensive guide, specifically focusing on training machine learning models using Python. Step-by-step instructions and explanations are provided to facilitate a thorough understanding of the training process. By following this article, beginners will gain practical knowledge and confidence in training their own machine learning models.

{"references": ["1.\tG\u00e9ron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media.", "2.\tLeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.", "3.\tNg, A. (n.d.). Machine Learning. Coursera. Retrieved from https://www.coursera.org/learn/machine-learning", "4.\tBrownlee, J. (n.d.). A Gentle Introduction to Machine Learning. Machine Learning Mastery. Retrieved from https://machinelearningmastery.com/gentle-introduction-machine-learning/", "5.\tGoodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Retrieved from http://www.deeplearningbook.org/"]}

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Keywords

machine learning, training, model, Python, beginners, optimization, parameters, predictions, unseen data, computer vision, natural language processing.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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