PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently. PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient. PyCaret is simple and easy to use.

AMP algorithms have two features that make them particularly attractive. First, they can easily be tailored to take advantage of prior information on the structure of the signal, such as sparsity or other constraints. Second, under suitable assumptions on a design or data matrix, AMP theory provides precise asymptotic guarantees for statistical procedures in the high-dimensional regime where the ratio of the number of observations n to dimensions p converges to a constant (Bayati and Montanari, 2012; Donoho et al., 2013; Sur et al., 2017). More generally, AMP has been also used to obtain lower bounds on the estimation error of first-order methods (Celentano et al., 2020), and in linear regression and low rank matrix estimation, it plays a fundamental role in understanding the performance gap between information-theoretically optimal and computationally feasible estimators (Reeves and Pfister, 2019; Barbier et al., 2019; Lelarge and Miolane, 2019). In these settings, it is conjectured that AMP achieves the optimal asymptotic estimation error among all polynomial-time algorithms (cf.

Learning optimal policies from historical data enables the gains from personalization to be realized in a wide variety of applications. The growing policy learning literature focuses on a setting where the treatment assignment policy does not adapt to the data. However, adaptive data collection is becoming more common in practice, from two primary sources: 1) data collected from adaptive experiments that are designed to improve inferential efficiency; 2) data collected from production systems that are adaptively evolving an operational policy to improve performance over time (e.g. contextual bandits). In this paper, we aim to address the challenge of learning the optimal policy with adaptively collected data and provide one of the first theoretical inquiries into this problem. We propose an algorithm based on generalized augmented inverse propensity weighted estimators and establish its finite-sample regret bound. We complement this regret upper bound with a lower bound that characterizes the fundamental difficulty of policy learning with adaptive data. Finally, we demonstrate our algorithm's effectiveness using both synthetic data and public benchmark datasets.

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My course will be your complete guide to the theory and applications of supervised & unsupervised machine learning and predictive modeling using the R-programming language. Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY MACHINE LEARNING & PREDICTIVE MODELS (K-means, Random Forest, SVM, logistic regression, etc) in R (many R packages incl. This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (classification & regressions) and unsupervised clustering techniques. Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain. In this age of big data, companies across the globe use R to analyze big volumes of data for business and research.

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Here is first part of the guide on implementing ML and AI solutions into manufacturing company. Following guide can be applied to all types of factories and products, which generate structured data preferable stored in the databases. It will work best for high volume products that get tested and have measurable output properties e.g.: resistance, latency, frequency, torque, power, energy consumption, pressure, speed, vibration, strength, clearance, efficiency, timing, thrust and all possible numeric or categorical properties that can be measured and are important for final characteristic and production yield. In terms of name for that system it could be called: Automated Production Optimisation or simply Digital Twin of Process and/or product. As you can imagine following examples suggest it can be used for things such as: PCB, Jet and rocket engines, gearboxes, combustion engines and all other mechanical, electronic, pneumatic and hydraulic devices.

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