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In , we provide a comprehensive guide to the fundamentals of . We start by explning what is and then delve into the core concepts such as supervised learning, unsupervised learning, and reinforcement learning. After that, we explore the various algorithms used in each of these categories, including linear regression, logistic regression, decision trees, random forests, support vector s, neural networks, and more.
We also discuss some important topics related to , such as overfitting, underfitting, model evaluation metrics like accuracy, precision, recall, F1 score, ROC curves, confusion matrices, feature selection, dimensionality reduction techniques like PCA and LDA, and hyperparameter tuning using methods such as grid search or random search.
Additionally, we provide a brief overview of some popular libraries and frameworks, including scikit-learn, TensorFlow, PyTorch, Keras, and others. We also highlight some real-world applications of in various domns like finance, healthcare, marketing, and more.
Finally, we discuss the ethical considerations of , such as bias, frness, privacy concerns, explnability, and transparency issues. provide a solid foundation for anyone who wants to learn about the basics of and how it works.
Rounded Up Text:
Explore our comprehensive guide on the essentials of which lays out its core concepts including supervised, unsupervised, and reinforcement learning alongside algorithms like linear regression, logistic regression, decision trees, random forests, support vector s, neural networks, among others.
We also highlight important topics such as overfitting vs underfitting, model evaluation metrics accuracy, precision, recall, F1 score, ROC curves, confusion matrices, feature selection techniques and dimensionality reduction methods including PCA and LDA, with hyperparameter tuning using grid search or random search.
Discover some popular libraries and frameworks like scikit-learn, TensorFlow, PyTorch, Keras and more. We also shed light on real-world applications in domns such as finance, healthcare, marketing, etc.
Finally, provide an ethical perspective onwith discussions around bias, frness, privacy concerns, explnability, transparency issues and other implications of algorithms.
This guide offers a solid starting point for anyone looking to understand the fundamentals of and its workings.
This article is reproduced from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572216/
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Comprehensive Machine Learning Basics Overview Core Concepts: Supervised Unsupervised Reinforcement Algorithms Explained: Linear Regression SVMs NNs Model Evaluation Metrics: Accuracy Precision Recall Bias and Fairness in Machine Learning Ethics Dimensionality Reduction Techniques: PCA LDA