How to Use Conversion-Rate (CVR) as an Objective in Multi-Armed Bandit Experiments. A step-by-step guide with code examples..Multi-armed bandit (MAB) has become an increasingly important tool for experimentation and has been widely adopted by the industry giants such as Google, Meta, Netflix, LinkedIn, etc. to conduct efficient experiments. However, widely-used MAB test designs require the objective of interest to provide instantaneous feedback in order to update the assignment probability to each variant. This is why most of the tutorials you can find for running MAB experiments are probably using Click-through rate (CTR) as an objective.
How to use causal influence diagrams to recognize the hidden incentives that shape an AI agent’s behaviorThere is rightfully a lot of concern about the fairness and safety of advanced Machine Learning systems. To attack the root of the problem, researchers can analyze the incentives posed by a learning algorithm using causal influence diagrams (CIDs). Among others, DeepMind Safety Research has written about their research on CIDs, and I have written before about how they can be used to avoid reward tampering. However, while there is some writing on the types of incentives that can be found using CIDs, I haven’t seen a succinct write up of the graphical criteria used to identify such incentives. To fill this gap, this post will summarize the incentive concepts and their corresponding graphical criteria,…
Advanced SQL Concepts You Should Know in 2022, master these time-saving, advanced SQL queries today. SQL or Structured Query Language is a must have tool for anyone who works with data. With the rising volume of data, the need for skilled data professionals is also increasing. Only knowledge of advanced SQL concepts is not enough, but you should be able to implement them at your work efficiently And that is what looked for in job interviews for data science positions! Therefore, I listed here 5 advanced SQL concepts with explanations and query examples which you should know in 2022.
Let’s check some high-level concepts that will fit a study plan for professionals that wants to know more about SQL and databases. As a data scientist, analyst, or engineer, you probably deal with tabular data pretty often. The row-column format is the most common way to store data as it is easy to grasp by most non-technical users. This two-dimensional format is showcased in several tools and frameworks such as: On the spreadsheet format in Microsoft Excel or Google Sheets.On the dataframe format in R or Python’s pandas.On tables stored on databases.A lot of data sources used for machine learning models are stored in the latter. Adding to that, a lot of the models’ output is also served through tables that fit in some kind of relational schema, helping users make sense of…
Stages, tips, and tools across the AI/ML lifecycleImage source: UnsplashMachine learning (ML) in the enterprise has been rapidly evolving over a narrow span. While it has grown exponentially, several organizations are still grappling to match the pace at which the ML stack is evolving. This end-to-end guide to the Modern AI Stack hopes to bring the best tools and practices under one common space to serve as a reference for: Leaders in the AI space including managers, directors, VPs, and CTOs who could learn more about new technologies/integrations and guide their teams toward the modern AI stackData Scientists and ML Engineers who could compare their current stack to our proposed AI stack and discover new technologies to adopt.But first, a walk down the memory lane…
You don’t want to waste time simply guessing what a prospect or customer wants and needs from your business or how they’ll react to a certain marketing campaign or strategy that you plan to implement. Rather, you can turn to the process of data-driven marketing to make informed decisions that are based on real data to ensure your marketing efforts are relevant to prospect and customer interests and behaviors.
Nirman Dave’s two startups are very different, but both have a DIY spirit. The first, called CircuiTricks and founded during his gap year after high school, created kits to teach students about electronics and physics. Now Dave is chief executive officer of Obviously AI, a no code AI/ML platform that enables people without technical backgrounds..
A deep-dive into Principal Coordinates Analysis with R and Python examples on Perceptual Mapping and Product BrandingPrincipal Coordinates Analysis — Photo by Calvin Hanson on UnsplashPrincipal Coordinates AnalysisIn this article, you will discover Principal Coordinate Analysis (PCoA), also known as Metric Multidimensional Scaling (metric MDS). You’ll learn what Principal Coordinates Analysis is, when to use it, and how to implement it on a real example using Python and/or R.
In this article, I describe the various steps involved in managing a machine learning process from beginning to end. Depending on which company you work for, you may or may not be involved in all the steps. In larger companies, you typically focus on one or two specialized aspects of a project. In small companies, you may be involved in all the steps. Here the focus is on large projects, such as developing a taxonomy, as opposed to ad-hoc or one-time analyses. I also mention all the people involved, besides machine learning professionals.
Machine Learning Operations (MLOps) is the primary way to increase the efficiency of Machine Learning workflows. Machine Learning is a big buzzword these days. Everyone seems to be wanting to jump into ML these days. Many new companies and startups implement ML projects in many ways. But only a part of these products is able to run and sustain themselves in the long run.
A thorough understanding of the basic components of programming language is vital in the development of any code. This article outlines all the fundamental concepts of the Python language. Just like how parts of speech form a basic building block of the English language, the following components form Python’s parts of speech.
Machine learning involves using an algorithm to learn and generalize from historical data in order to make predictions on new data. This problem can be described as approximating a function that maps examples of inputs to examples of outputs. Approximating a function can be solved by framing the problem as function optimization. This is where a machine learning algorithm defines a parameterized mapping function (e.g. a weighted sum of inputs) and an optimization algorithm is used to fund the values of the parameters (e.g. model coefficients) that minimize the error of the function when used to map inputs to outputs.
Sentiment Analysis — Comparing 3 Common Approaches: Naive Bayes, LSTM, and VADERA Study on Strengths and Drawbacks for the Different Approaches (With Sample Code) Sentiment Analysis, or Opinion Mining, is a subfield of NLP (Natural Language Processing) that aims to extract attitudes, appraisals, opinions, and emotions from text. Inspired by the rapid migration of customer interactions to digital formats e.g. emails, chat rooms, social media posts, comments, reviews, and surveys, Sentiment Analysis has become an integral part of analytics organizations must perform to understand how they are positioned in the market. To be clear, Sentiment Analysis isn’t a novel concept.
In recent years, researchers have used artificial intelligence to improve translation between programming languages or automatically fix problems. The AI system DrRepair, for example, has been shown to solve most issues that spawn error messages. But some researchers dream of the day when AI can write programs based on simple descriptions from non-experts.
There are tons of resources and links out there, but often we get confused on which resources to follow. Don’t worry, I have got you covered. I have attached the links to several YouTube channels, blogs, courses, and other websites that I found appropriate for a beginner. You can also use the Data Science Community Websites Like Analytics Vidhya and Kaggle for implementing your learning and getting hands-on experience in Data Science.
Is your organization interested in taking success measurement to another level? As brands are competing more based on their digital experience, stakeholders are very keen to understand the business impact of investing in this area. In this insight article, Data Analyst, Andrew Millett, discusses how your organization can measure the impact of changing the digital experience and how it affects your business goals.
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