Plotting

Directed Networks


Popular Machine Learning Algorithms - KDnuggets

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When starting out with Data Science, there is so much to learn it can become quite overwhelming. This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R. Linear Regression is the simplest Machine learning algorithm that branches off from Supervised Learning. It is primarily used to solve regression problems and make predictions on continuous dependent variables with the knowledge from independent variables. The goal of Linear Regression is to find the line of best fit, which can help predict the output for continuous dependent variables.



Top Posts April 25 - May 1: 15 Python Coding Interview Questions You Must Know For Data Science - KDnuggets

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Decision Tree Algorithm, Explained by Nagesh Singh Chauhan Naïve Bayes Algorithm: Everything You Need to Know by Nagesh Singh Chauhan Why Are So Many Data Scientists Quitting Their Jobs? by Natassha Selvaraj 8 Free MIT Courses to Learn Data Science Online by Natassha Selvaraj Top Programming Languages and Their Uses by Claire D. Costa Python Libraries Data Scientists Should Know in 2022 by Nisha Arya The Complete Collection Of Data Repositories – Part 1 by Abid Ali Awan 15 Python Coding Interview Questions You Must Know For Data Science by Nate Rosidi 5 Different Ways to Load Data in Python by Ahmad Anis DBSCAN Clustering Algorithm in Machine Learning by Nagesh Singh Chauhan Why Are So Many Data Scientists Quitting Their Jobs? by Natassha Selvaraj Top Posts Jan 10-16: Is Data Science a Dying Career? Free Metis Corporate Training Series: Intro to Python, Continued Free Metis Corporate Training Series: Intro to Python KDnuggets Top Posts for January 2022: Is Data Science a Dying Career? Top Posts Jan 31 - Feb 6: 7 Steps to Mastering Machine Learning with Python… Top Posts Jan 3-9: Why Do Machine Learning Models Die In Silence? Top Posts Jan 10-16: Is Data Science a Dying Career? KDnuggets Top Posts for January 2022: Is Data Science a Dying Career?


Data Science: Supervised Machine Learning in Python

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In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


9 Completely Free Statistics Courses for Data Science

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This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.


Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography

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Photoacoustic tomography (PAT) is a hybrid biomedical imaging modality based on the photoacoustic effect [6, 44, 32]. In PAT, the imaged target is illuminated with a short pulse of light. Absorption of light creates localized areas of thermal expansion, resulting in localized pressure increases within the imaged target. This pressure distribution, called the initial pressure, relaxes as broadband ultrasound waves that are measured on the boundary of the imaged target. In the inverse problem of PAT, the initial pressure distribution is estimated from a set of measured ultrasound data.


The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review

Journal of Artificial Intelligence Research

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.


Machine Learning Bootcamp: SVM,Kmeans,KNN,LinReg,PCA,DBS

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The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios. UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data.


A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

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COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods. Natural language processing (NLP) techniques have been developed to assess peoples' sentiments on various topics.


Bayesian Estimation of Nelson-Siegel model using rjags R package

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