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Forbes AI stats* show that 86% of consumers prefer Humans to Chatbots. This means the consistency of AI-generated responses is crucial for building trust with users and maintaining brand reputation especially when the chatbot industry is likely to reach $1.34 Billion in 2024. This Short Course was created to help AI developers, data scientists, and product managers accomplish the goal of achieving consistent and coherent responses from OpenAI's large language models. By completing this course, you'll be able to enhance the reliability of AI-generated responses, improve user satisfaction, and boost the overall performance of AI applications. You'll also gain practical techniques to ensure consistency in AI responses, allowing you to apply these skills immediately in your projects. More specifically, in this 2-hour long course, you will learn how to fine-tune OpenAI's large language models for specific contexts, apply post-processing techniques to refine responses, implement prompt engineering strategies for clear and effective communication, and analyze temperature and sampling parameters for optimal response consistency. This project is unique because it provides a comprehensive overview of strategies for achieving consistent responses with OpenAI's large language models, coupled with practical techniques and real-world examples. In order to be successful in this project, you will need a basic understanding of natural language processing and machine learning concepts.

Data Engineering is all about efficient data collection, generation, transformation and storage. Generative AI tools have the capability of making each of data engineering tasks more efficient, effective, and convenient on an ETL pipeline. This specialization is designed not only for Data Engineers but for anyone who might be interested in the use of generative AI in Data Engineering. With three self-paced courses in the specialization, you will begin with learning the differences that distinguish generative AI from discriminative AI. You'll delve into real-world generative AI use cases and explore popular generative AI models and tools for text, code, image, audio, and video generation. Next, delve into generative AI prompts engineering concepts and real-world business uses . Learn about prompt techniques like zero-shot and few-shot and explore various prompt engineering approaches and explore commonly used prompt engineering tools including IBM Watsonx, Prompt Lab, Spellbook, and Dust. No experience is needed to begin this specialization, although you might find it helpful to have some data engineering knowledge. Take your career to the next level. Let's get started! Applied learning project This Specialization emphasizes applied learning and includes a series of hands-on activities and projects . In these exercises, you'll take the theory and skills you've gained and practice them with real-world scenarios . You will perform hands-on labs to: Generate text, images, and code using Generative AI Apply prompt engineering techniques and best practices Use generative AI tools to design data workflow

With generative AI, a new technology era has begun. Software developers can leverage this revolutionary technology to write high-quality code with fewer bugs, which will increase their overall effectiveness and efficiency and. This is making generative AI an essential and must-have skill for software engineers. This IBM specialization offering is designed for anyone interested in leveraging the power of generative AI in software development. This includes existing and aspiring web developers, mobile app developers, front-end developers, backend-end developers, full stack developers, DevOps professionals, and Site Reliability Engineers (SREs). With three self-paced courses in the specialization, you will begin with the basics of generative AI including its uses, models, and tools for text, code, image, audio, and video generation. Advance to prompts engineering , explore various prompt engineering approaches and prompt engineering tools including IBM Watsonx, Prompt Lab, Spellbook, and Dust. Boost your programming skills by learning to leverage generative AI to design, develop, translate, test, document, and launch applications and their code. Gain hands-on experience using generative AI tools and models , such as GitHub Copilot, Open AI ChatGPT, and Google Gemini, for various software engineering tasks.   Enroll now and gain the edge with AI skills that will boost your productivity and effectiveness as a programmer . Applied learning project This Specialization emphasizes applied learning and includes a series of hands-on activities and projects . In these exercises, you'll take the theory and skills you've gained and practice them with real-world scenarios . Projects include: Generate Text, Images, and Code using Generative AI Apply Prompt Engineering Techniques and Best Practices Create a Personalized Learning Platform for Software Developers using ChatGPT

Learning Outcomes: This introductory-level course combined theoretical knowledge with actionable insights for real-world business scenarios, making it ideal for learners looking to integrate AI into the social ethics strategy. By the end of this course, you will be able to: • Understand AI Basics • Explore Ethical Dimensions • Grasp Regulatory Environments • Address Privacy and Fairness: • Practical Insights This course covers an Intermediate knowledge of AI technology and its current application in the business field. It also covers fundamental topics of ethics and regulatory frameworks relevant to AI issues. The course also provides real-world case studies and future projections. This course integrates AI technology with business applications and social ethics, providing a unique and comprehensive learning experience. Participants will gain practical skills for implementing AI solutions responsibly and in compliance with regulations. The course connects theoretical concepts to actual business and ethical scenarios through case studies and real-world examples. Discussions on future AI trends and ethical dilemmas further prepare learners to lead in their fields. This Intermediate-level course is designed for students and professionals in various fields, including business managers, IT specialists, policymakers, and those aspiring to integrate AI into their career paths. To be successful in this course, you need to have a background in fundamental business operations and basic knowledge of AI (common AI applications in business and basic algorithms).

The GenAI Academy offers an engaging course designed specifically for Digital Marketing Specialists, focusing on how Generative Artificial Intelligence (GenAI) can revolutionize marketing strategies. This program provides you with essential knowledge and skills to creatively and effectively leverage GenAI in your projects. This course is designed for team leaders and managers seeking to enhance team productivity and creativity with GenAI, digital marketing specialists focused on improving content creation and campaign management, and career transitioners aiming to specialize in digital marketing and stay competitive by mastering GenAI technologies. While no prior AI expertise is needed, familiarity with popular social media platforms (eg, Facebook, Instagram, Twitter, LinkedIn), strong communication and data analysis skills, and proficiency in content creation will enhance your learning experience. Embracing creativity and having a solid understanding of fundamental marketing concepts are also beneficial. Upon successful completion of the curriculum, students at any proficiency level will be well prepared to seamlessly integrate GenAI into their marketing strategies, leading to increased efficiency, productivity and favorable outcomes in their digital marketing endeavors.

Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today's user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. This course offers a step-by-step guide to building a fully functional and responsive website for a real client. Starting from understanding client requirements, the course shows all aspects of website development. It covers topics from lessons on coding a website header and navigation menu, crafting a responsive homepage and menu page using Bootstrap and CSS to resolving potential website resize issues for a seamless mobile experience. The course emphasizes hands-on learning, guiding learners in creating a restaurant website. Through building the restaurant website, learners will gain expertise in requirements gathering, website design and development, responsive web design, and ensuring a visually appealing and user-friendly website that works across all devices.

This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation. This course is ideal for AI Product Managers looking to optimize LLM applications, Data Scientists interested in advanced AI model evaluation techniques, AI Ethicists and Policy Makers focused on responsible AI deployment, and Academic Researchers studying the impact of generative AI across various domains. A basic understanding of artificial intelligence, machine learning concepts, and familiarity with natural language processing (NLP) is recommended. Prior experience with Google Cloud Vertex AI is beneficial but not required. It covers practical applications, integrating human judgment with automatic methods, and prepares learners for future trends in AI evaluation across various media, including text, images, and audio. This comprehensive approach ensures you are equipped to assess LLMs effectively, enhancing business strategies and innovation.

This course gives you a thorough introduction to the python programming language. You'll start by learning the definition of python, its history, semantics, and how it works. The first week concludes by writing a python script to solve a problem. In Week 2, you'll learn about Serverless Architectures and understand the differences between microservices, service-oriented and monolith architectures. You'll build a microservices architecture by learning how to use the following tools: AWS Lambda, Amazon API Gateway, and Cloud9. It's then time to go deep with AWS Databases. You'll see Amazon RDS and Amazon DynamoDB in action, visualizing the important role they play in developing applications in python. The course's final week will cover application integration services. Amazon's Simple Notification Service (SQS), Simple Queue Service (SQS), and EventBridge take center stage as important “glue” components for scalable, cloud-based applications. You'll close the course with a hands-on project, writing some code to use some hardcoded data to write to dynamodb and then convert that into a lambda function.

This comprehensive course is tailored specifically for data analysts, business professionals, and anyone looking to significantly enhance their proficiency in Microsoft Excel for complex data analysis tasks. By exploring a wide range of advanced Excel features and techniques, participants will gain the ability to analyze and interpret data to make informed business decisions. Learners will be introduced to real-world applications, using Excel to tackle diverse scenarios. The main case study will focus on Marketing, specifically the pricing of diamonds based on their characteristics and analyzing production quality data in manufacturing. By the end of the case study, learners will feel empowered to apply regression analysis to problems in their own field. This practical approach ensures that learners will leave the course equipped to handle complex data analysis challenges, providing them with the skills to conduct hypothesis testing, regression analysis, and more. The course is designed for Business Analysts and Data Analysts seeking to enhance their analytical skills and improve their decision-making capabilities. It is ideal for professionals who regularly handle data, generate reports, and provide insights to support business strategies. Participants are expected to have a keen interest in leveraging data to drive organizational success and be open to learning advanced analytical techniques and tools. Participants should be proficient in Microsoft Excel for data manipulation, analysis, and visualization. They should also understand fundamental statistical concepts, including measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and confidence intervals. This foundation will help them grasp more complex analytical methods and apply them effectively in their roles. By the end of this course, learners will have mastered the necessary tools to effectively visualize, analyze, and interpret data using Excel, making them invaluable assets in their professional fields. This course will empower participants to transform raw data into compelling insights, driving business strategies and achieving better outcomes.

The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. This course stands out for its balance between fundamental concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.

The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.

In the course "Artificial Intelligence in Social Media Analytics", learners will explore the intersection of artificial intelligence and social media analytics, equipping them with essential skills to navigate and analyze digital landscapes. By delving into machine learning fundamentals, natural language processing, sentiment analysis, and topic modeling, participants will gain practical experience in applying AI techniques to real-world social media data. This course stands out by providing not only theoretical insights but also hands-on opportunities to construct classifiers, perform sentiment analysis, and build semantic networks, all tailored to the complexities of social media content. As learning progress, they will develop a keen understanding of how AI can uncover hidden patterns, sentiment, and topics within vast amounts of unstructured data. The unique blend of fundamental concepts and practical applications ensures that participants can effectively analyze social media interactions and derive actionable insights. Whether for career advancement or personal interest, this course offers a comprehensive toolkit to leverage AI for understanding social dynamics and enhancing engagement strategies in digital platforms.