Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previous works, and direct LLM application suffers from strategic brittleness and factual hallucination. In this paper, we first construct and release TeleSalesCorpus, the first real-world-grounded dialogue dataset for this domain. We then propose AI-Salesman, a novel framework featuring a dual-stage architecture. For the training stage, we design a Bayesian-supervised reinforcement learning algorithm that learns robust sales strategies from noisy dialogues. For the inference stage, we introduce the Dynamic Outline-Guided Agent (DOGA), which leverages a pre-built script library to provide dynamic, turn-by-turn strategic guidance. Moreover, we design a comprehensive evaluation framework that combines fine-grained metrics for key sales skills with the LLM-as-a-Judge paradigm. Experimental results demonstrate that our proposed AI-Salesman significantly outperforms baseline models in both automatic metrics and comprehensive human evaluations, showcasing its effectiveness in complex persuasive scenarios.

VIRTUAL BOOTCAMPS Python for Data Science & Machine Learning Python is a programming language widely used by Data Scientists. This Summer Bootcamp is for those who want to start a career in Data Science and those who want to learn more about using Python for Data Science and Machine Learning. The Summer Bootcamp will cover

New businesses are creating innovative financial services and banking solutions that will make the Fintech market more competitive. The Market Forecast predicts that the fintech market will grow at 23.41% annually to $324 billion by 2026. The industry is growing steadily and fraudsters are evolving at the same time, making it difficult to provide the best tools for protecting customers and companies to financial institutions. Customers remain skeptical because financial institutions are slow to implement tools. According to the BIS survey, May 2021, US households trust traditional banks more than Fintech for protecting their data.

Data Science is a broad term that encompasses multiple disciplines. It is a rapidly growing field of study that uses scientific methods to extract meaningful insights from given input data. The rapid growth in the field of data science has opened the eyes of researchers interested in this field to explore more into the multiple disciplines that encompass data science. Let us discuss a few of these broad areas that are fundamental aspects to be covered for mastering Data science. Machine Learning: Both Machine Learning and Data Science are buzzwords in today's technical world.

This course is Very Practical, Easy to Understand and Every Concept is Explained with an Example! I have added real life examples to understand the applications of statistics in the field of Data Science... We'll cover everything that you need to know about statistics and probability for Data Science and Business Analytics! So What Are You Waiting For?

According to statistics, AI projects fail at a high rate. With us, experience working with various clients has taught us that AI projects necessitate an entirely different strategy than normal mobile/web apps. This article defines the high-level method for effectively designing successful AI-powered applications. The International Data Corporation (IDC quotes as half of all Artificial Intelligence (AI) efforts fail. This is not the only accusation made by the IDC.

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Video continues to be top-of-mind for companies because the consumers have an insatiable appetite for watching videos. Cisco estimates that video will account for 82% of global internet traffic in 2022.

With the rise of digital marketing led by tools including Google Analytics, Google Adwords, and Facebook Ads, a key competitive advantage for businesses is using A/B testing to determine effects of digital marketing efforts. In short, small changes can have big effects. This is why A/B testing is a huge benefit. A/B Testing enables us to determine whether changes in landing pages, popup forms, article titles, and other digital marketing decisions improve conversion rates and ultimately customer purchasing behavior. A successful A/B Testing strategy can lead to massive gains - more satisfied users, more engagement, and more sales - Win-Win-Win. A major issue with traditional, statistical-inference approaches to A/B Testing is that it only compares 2 variables - an experiment/control to an outcome. The problem is that customer behavior is vastly more complex than this. Customers take different paths, spend different amounts of time on the site, come from different backgrounds (age, gender, interests), and more. This is where Machine Learning excels - generating insights from complex systems.

A complete hands on practical exercises to build regression models that are highly used for business analysis. This course is designed to start with the very basics then add up information gradually. Accordingly students who have fair background in regression analysis can choose to jump to the practical part of the course to learn building regression models in detail. In this course you will learn about different types of regression models and learn to build and use the ones used in business analysis. You will learn step by step how to understand a business problem from data observations and determine the variables you need to include in regression analysis.