Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention. However, the sheer number of available prompt engineering techniques creates an overwhelming landscape for practitioners looking to utilize these tools. For the most efficient and effective use of LLMs, it is important to compile a comprehensive list of prompting techniques and establish a standardized, interdisciplinary categorization framework. In this survey, we examine some of the most well-known prompting techniques from both academic and practical viewpoints and classify them into seven distinct categories. We present an overview of each category, aiming to clarify their unique contributions and showcase their practical applications in real-world examples in order to equip fellow practitioners with a structured framework for understanding and categorizing prompting techniques tailored to their specific domains. We believe that this approach will help simplify the complex landscape of prompt engineering and enable more effective utilization of LLMs in various applications. By providing practitioners with a systematic approach to prompt categorization, we aim to assist in navigating the intricacies of effective prompt design for conversational pre-trained LLMs and inspire new possibilities in their respective fields.

Clustering is a technique used in machine learning and data analysis to group similar data points together. The goal of clustering is to identify patterns and relationships in the data without any prior knowledge of the underlying structure. Clustering is commonly used in unsupervised learning, where the algorithm is not given any labeled data and must find its own structure in the data. There are numerous applications of clustering in various fields such as finance, marketing, biology, social networks, image and video processing, and many more. There are several different algorithms that can be used for clustering, including k-means, hierarchical clustering, and DBSCAN.

ChatGPT is an advanced chatbot created by OpenAI, a company that created GPT-3. Users can ask ChatGPT open-ended questions about any topic and receive responses generated specifically for the question. I've already discussed what this tool is capable of, but in this article, I want to explore how product creators can make the most of this tool. I will use ChatGPT to create assets for the new website (a landing page for a robot vacuum cleaner) -- eight practical tasks in total, along with my impression of how well ChatGPT can deal with them. A product brief outlines key product information that a product team uses to build a new product/feature.

Artificial Intelligence (AI) has immensely impacted digital marketing, allowing businesses and marketers to control and analyze vast sets of consumer data without the need for direct human interventions. As consumer market trends change due to social influence, digital marketers are left leveraging the digital capabilities of AI to attract, retain and engage with customers in a more personalized frequency. Research reveals that digital marketers spend around 15% of the departmental budget on AI-related tools, and while these monetary efforts have grown at stratospheric rates, more than a third of marketers still say they are unable to properly measure the impact these AI tools have on their direct business. Ongoing development of AI capabilities gives businesses a better understanding of who their target customer audience is, and how they can improve their engagement through meaningful content efforts. With the global digital marketing software sector expected to grow to more than $67.53 billion in 2022, up from $56.77 billion in 2021, here's a review of the five best use cases of how AI has helped transform digital marketing for businesses.

The world is driven by connections--from financial and communication systems to social and biological processes. As connectedness continues to accelerate, it's not surprising that interest in graph algorithms has exploded because they are based on mathematics explicitly developed to gain insights from the relationships between data. Graph analytics can uncover the workings of intricate systems and networks at massive scales--for any organization. We are passionate about the utility and importance of graph analytics as well as the joy of uncovering the inner workings of complex scenarios. Until recently, adopting graph analytics required significant expertise and determination, because tools and integrations were difficult and few knew how to apply graph algorithms to their quandaries.

This article was published as a part of the Data Science Blogathon. You might be wandering in the vast domain of AI, and may have come across the word Exploratory Data Analysis, or EDA for short. Is it something important, if yes why? If you are looking for the answers to your question, you're in the right place. Also, I'll be showing a practical example of an EDA I did on my dataset recently, so stay tuned! Exploratory Data Analysis is the critical process of conducting initial investigations on data to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics and graphical representations.

When working on a machine learning project, you need to follow a series of steps until you reach your goal. One of the steps you have to perform is hyperparameter optimization on your selected model. This task always comes after the model selection process where you choose the model that is performing better than other models. Before I define hyperparameter optimization, you need to understand what a hyperparameter is. In short, hyperparameters are different parameter values that are used to control the learning process and have a significant effect on the performance of machine learning models. An example of hyperparameters in the Random Forest algorithm is the number of estimators (n_estimators), maximum depth (max_depth), and criterion. These parameters are tunable and can directly affect how well a model trains. So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time.

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A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems -- yet, is mostly used for classification problems. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions. Before we dive deep into the working principle of the decision tree's algorithm you need to know a few keywords related to it. Attribute Subset Selection Measure is a technique used in the data mining process for data reduction.

It brings a fresh, unique focus on sketches, often overlooked in monographs, as well as its highly practical, hands-on grounding in the open-source MOA system. Not to be missed by anyone with serious interest in Big Data and Data Science.