The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention. One of the main challenges in applying ML to previously unseen problems is algorithm selection - the identification of high-performing algorithm(s) for a given dataset, task, and evaluation measure. This study addresses the algorithm selection challenge for data clustering, a fundamental task in data mining that is aimed at grouping similar objects. We present MARCO-GE, a novel meta-learning approach for the automated recommendation of clustering algorithms. MARCO-GE first transforms datasets into graphs and then utilizes a graph convolutional neural network technique to extract their latent representation. Using the embedding representations obtained, MARCO-GE trains a ranking meta-model capable of accurately recommending top-performing algorithms for a new dataset and clustering evaluation measure. Extensive evaluation on 210 datasets, 13 clustering algorithms, and 10 clustering measures demonstrates the effectiveness of our approach and its dominance in terms of predictive and generalization performance over state-of-the-art clustering meta-learning approaches.

With the variational lower bound of mutual information (MI), the estimation of MI can be understood as an optimization task via stochastic gradient descent. In this work, we start by showing how Mutual Information Neural Estimator (MINE) searches for the optimal function $T$ that maximizes the Donsker-Varadhan representation. With our synthetic dataset, we directly observe the neural network outputs during the optimization to investigate why MINE succeeds or fails: We discover the drifting phenomenon, where the constant term of $T$ is shifting through the optimization process, and analyze the instability caused by the interaction between the $logsumexp$ and the insufficient batch size. Next, through theoretical and experimental evidence, we propose a novel lower bound that effectively regularizes the neural network to alleviate the problems of MINE. We also introduce an averaging strategy that produces an unbiased estimate by utilizing multiple batches to mitigate the batch size limitation. Finally, we show that $L^2$ regularization achieves significant improvements in both discrete and continuous settings.

Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform many state-of-the-art methods. But GAIN only uses a reconstruction loss in the generator to minimize the imputation error of the non-missing part, ignoring the potential category information which can reflect the relationship between samples. In this paper, we propose a novel unsupervised missing data imputation method named PC-GAIN, which utilizes potential category information to further enhance the imputation power. Specifically, we first propose a pre-training procedure to learn potential category information contained in a subset of low-missing-rate data. Then an auxiliary classifier is determined based on the synthetic pseudo-labels. Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results. The proposed method can significantly improve the imputation quality of GAIN. Experimental results on various benchmark datasets show that our method is also superior to other baseline models.

Knowing the mathematics behind machine learning algorithms is a superpower. If you have ever built a model for a real-life problem, you probably experienced that being familiar with the details can go a long way if you want to move beyond baseline performance. This is especially true when you want to push the boundaries of state of the art. However, most of this knowledge is hidden behind layers of advanced mathematics. Understanding methods like stochastic gradient descent might seem difficult since it is built on top of multivariable calculus and probability theory.

Supervised Learning is an essential part of Machine Learning. Classification techniques are used when the variable to be predicted is categorical. A common example of classification problem is trying to classify an Iris flower among its three different species. Logistic regression is a classification technique borrowed by machine learning from the field of statistics. Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.

Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.

Complete Machine Learning with R Studio - ML for 2020 - Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio Created by Start-Tech AcademyPreview this Course - GET COUPON CODE You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right? You've found the right Machine Learning course! After completing this course you will be able to: · Confidently build predictive Machine Learning models to solve business problems and create business strategy · Answer Machine Learning related interview questions · Participate and perform in online Data Analytics competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

Complete course for Data Science and Machine Learning with R From beginner to expert in Data Science techniques with R: machine learning, neural networks, text mining What you'll learn Description This course on Data Science with R was created to be a complete path on how data analysis has evolved in recent years starting from classical algebra and statistics. The goal is to accompany a student who has some basic R on a journey through the various souls of Data Science. We will start with a review of the basics of R, starting with downloading and installing, setting up the work environment, going through structures, creating functions, using operators and some important functions. We will then move on to see how to manipulate and manage a dataset, extract cases or variables, generate random datasets, calculate basic statistical measures, create graphs with the Matplotlib and Seaborn packages. In the following sections we begin to enter the heart of Data Science with R, starting with preprocessing: we see how to clean up and normalize a dataset, and how to manage missing data.

Preview this course - GET COUPON CODE Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) Algorithmic trading (VIP only) Statistical Factor Models (VIP only) Regime Detection with Hidden Markov Models (VIP only) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.

This course on Data Science with Python was created to be a complete path on how data analysis has evolved in recent years starting from classical algebra and statistics. The goal is to accompany a student who has some basic Python on a journey through the various souls of Data Science. We will start with a review of the basics of Python, starting from downloading and installing, setting up the work environment, going through structures, creating functions, using operators and some important functions. We will then move on to see how to manipulate and manage a dataset, extract cases or variables, generate random datasets, calculate basic statistical measures, create graphs with the Matplotlib and Seaborn packages. In the following sections we begin to enter the heart of Data Science with Python, starting with preprocessing: we see how to clean up and normalize a dataset, and how to manage missing data.