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This course will give you a fundamental understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.), and demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition and text prediction. In addition, we have developed practical exercises that will allow you to gain hands-on experience in applying these data science models to data sets. These hands-on exercises will teach you how to implement machine learning algorithms using PyTorch, an open source library used by leading technology companies in the field of machine learning (e.g. Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many others).

This course will help you become a better negotiator. Unlike many negotiation courses, we develop a framework for analyzing and shaping negotiations. This framework will enable you to make principled arguments that persuade others. It will enable you to look beneath the surface of apparent conflicts to discover underlying interests. By the end of the course, you will be better able to predict, interpret, and shape the behavior of those you encounter in competitive situations. Throughout this course, you will have several opportunities to negotiate with other students using examples based on common situations in business and life. You will be able to receive feedback on your performance and compare what you did with how others approached the same scenario. These examples also provide an opportunity to discuss a wide range of topics, including preparing for a negotiation, making ultimatums, avoiding regret, expanding the pie, and communicating with someone who has a completely different world view. Additional topics include negotiating when you don’t have the power, negotiating over email, and the role of gender differences in negotiations. To round out the course, we'll hear from three negotiation experts: Linda Babcock, Herb Cohen, and John McCall McBain. Enjoy.

With the advent of Generative AI, data engineering processes have undergone an amazing transformation. In this course, you will learn the impact of Generative AI on data engineering. As a data engineer, you will be able to use Generative AI to improve productivity by implementing innovative ways to deliver projects. Data engineering is responsible for building powerful data pipelines, managing data infrastructure, and ensuring high-quality data insights. This course is suitable for existing and aspiring data engineers, data warehousing specialists, and other data professionals such as data analysts, data scientists, and BI analysts. You will learn how to use and apply generative models to solve problems such as architecture design, database querying, data warehouse schema design, data augmentation, data pipelines, ETL workflows, data analysis and mining, data lakes, and data warehouses. You will also explore the challenges and ethical considerations associated with using Generative AI. Demonstrate your new Generative AI skills in a hands-on data engineering project that you can apply in your real life. Then take a final test to earn a certificate. You will be able to present your project and certificate to your current or future employers.

This course gives a broad overview of the impact that generative artificial intelligence (AI) could have on jobs and how it can be used to support everyday tasks. As the use of generative AI increases, organizations are finding new ways to augment their work, impacting the number and types of jobs we can expect in the future. In “Generative AI: Labor and the Future of Work,” you’ll explore how generative AI can support workers in all industries, as well as the labor that goes into developing and managing these systems. Explore what the “future of work” could mean for both employees and employers and how these systems can impact work quality, creativity, and the labor market. You’ll also look at how the global digital divide could impact tool adoption for different groups across the world. By analyzing the adoption and implementation of generative AI in the workplace, you will better understand how these tools could shift how we work together.

We all negotiate every day. On a personal level, we negotiate with friends, family members, landlords, car dealers, employers, etc. Negotiation is also the key to success in business. No business can survive without lucrative contracts. Within a company, negotiation skills can lead to your career advancement. I hope you will join the hundreds of thousands of listeners who have made Successful Negotiations one of the most popular and top-rated MOOCs around the world. During the course, you will learn and put into practice the four steps to successful negotiation: (1) Preparation: Plan your negotiation strategy (2) Negotiate: Use key tactics for success (3) Closing: Close the contract (4) Complete and Assess: The End Game To successfully complete this course and improve your negotiation abilities, you will need to do the following: (1) Watch short videos (5 to 20 minutes in length). The videos are interactive and contain questions to test your understanding of strategy and negotiation skills. You can speed up or slow down the videos to choose a listening pace that suits you. Depending on your schedule, you can watch the videos over several weeks, or you can watch them “in one go.” One student who watched the course in one sitting concluded that “It's as good as Breaking Bad.” Another student compared the course to “House of Cards.” Both series contain interesting examples of difficult negotiations! (2) Test your negotiation skills by completing negotiations in Module 6. You can negotiate with a local acquaintance or use Discussions to find a partner from another part of the world. Your negotiation partner will give you feedback on your negotiation skills. To help you with your negotiations, I have developed several free negotiation planning tools related to the course. These tools and a free app are available at http://negotiationplanner.com/. The course also includes an interactive experience where you can test your negotiation skills before engaging in real negotiations. (3) Pass the final exam. To successfully complete the course, you must answer 80% of the questions correctly. The exam is a mastery test, which means you can take it as many times as you like until you have mastered the material. Course Certificate You have the opportunity to receive a Course Certificate. The certificate provides official recognition of your achievements in the course and includes the University of Michigan logo. You can find out more about certificates at: https://learner.coursera.help/hc/en-us/articles/209819053-Get-a-Course-Certificate This course is also available in Spanish and Portuguese.To join the fully translated Spanish version, visit this page: https://www.coursera.org/learn/negociacion/ To join the fully translated Portuguese version, visit this page: https://www.coursera.org/learn/ negociacao Subtitles for video materials are available in English, Ukrainian, Chinese (simplified), Portuguese (Brazilian), Spanish Created by: University of Michigan Composite course logo is distributed under the Creative Commons CC BY-SA license and was created using images kindly provided by Flazingo Photos and K2 Space.

This is an introductory course on micro-research destinations that explains what IA is responsible for, why it is important and how Google implements it in its products. Here are the 7 principles of Google IA.

Learn key principles of designing effective AI agents, and organizing a team of AI agents to perform complex, multi-step tasks. Apply these concepts to automate 6 common business processes. Learn from João Moura, founder and CEO of crewAI, and explore key components of multi-agent systems: 1. Role-playing: Assign specialized roles to agents 2. Memory: Provide agents with short-term, long-term, and shared memory 3. Tools: Assign pre-built and custom tools to each agent (e.g. for web search) 4. Focus: Break down the tasks, goals, and tools and assign to multiple AI agents for better performance 5. Guardrails: Effectively handle errors, hallucinations, and infinite loops 6. Cooperation: Perform tasks in series, in parallel, and hierarchically Throughout the course, you’ll work with crewAI, an open source library designed for building multi-agent systems. You’ll learn to build agent crews that execute common business processes, such as: 1. Tailor resumes and interview prep for job applications 2. Research, write and edit technical articles 3. Automate customer support inquiries 4. Conduct customer outreach campaigns 5. Plan and execute events 6. Perform financial analysis By the end of the course, you will have designed several multi-agent systems to assist you in common business processes, and also studied the key principles of AI agent systems.

Generative AI knowledge is now an essential skill in project management. According to Business 2 Community, 93% of companies that invest in AI for project management report a positive return on investment. Additionally, generative AI can improve the success rate of projects by around 25%. Reflecting on the industry’s direction, IBM introduces this specialization to boost your career in project management. This specialization consisting of short self-paced courses is designed to help you kickstart your journey into applying generative AI within project management practices. This program is ideal for current and aspiring project professionals, including project managers, project coordinators, scrum masters, and those passionate about the field. You will begin the specialization by understanding the basics of generative AI and its real-world applications. You will then learn about generative AI prompts engineering concepts and approaches, and explore commonly used prompt engineering tools including IBM watsonx Prompt Lab, Spellbook, and Dust. Finally, you will explore how to apply generative AI techniques and tools, such as ChatGPT, Copilot, Gemini, and DALL-E throughout the project management lifecycle. Enhance your project management capabilities with generative AI! Enroll today and show your current and prospective employers your readiness to adeptly leverage the transformative power of generative AI in project management. 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. Some projects include: - - Generate text, images, and code using generative AI - Apply prompt engineering techniques and best practices - Apply generative AI tools to improve project management performance

Dive into the revolutionary world of Large Language Models (LLMs) with our comprehensive 4-hour workshop, designed to bridge the gap between theoretical knowledge and practical skills. Whether you're a budding data scientist, an AI enthusiast, or a seasoned professional looking to expand your toolkit, this course is tailored to empower you with hands-on experience in leveraging LLMs for a variety of real-world applications. What You'll Learn: - Fundamentals and Advanced Techniques: Start with the basics of Large Language Models, including their architecture and capabilities, before progressing to advanced optimization methods such as Quantization and LoRA. - Practical Exercises: Engage in structured exercises using Kaggle datasets in Colab, fine-tuning models for tasks like question answering and text summarization with QLoRA, and exploring cutting-edge concepts such as Retrieval Augmented Generation (RAG). - Real-World Applications: Tackle engaging projects like building a semantic search engine to find movies and developing a chat interface with scholarly articles, applying your knowledge in tangible, impactful ways. - Model Publication: As a bonus, learn how to share your fine-tuned models with the world through Huggingface, enhancing your visibility in the AI community. Intended Learners: This course is perfect for individuals looking to deepen their understanding of LLMs and apply these models in innovative ways. Ideal for AI professionals, data scientists, and researchers eager to expand their skills and apply LLMs to solve complex problems.

Unlock the Hidden Potential of Large Language Models with this Google Cloud Course! Step into the transformative realm of language models and learn how to harness their expansive potential with Google Cloud and Python. This in-depth Udemy course offers a perfect fusion of theoretical insights and practical skills. About the Course: The course kicks off with a solid foundation in Large Language Models, helping students understand their complexity and functioning. As you delve into the main modules, you will become proficient in using the Google Cloud platform, effortlessly navigating the Generative AI Studio, comprehending its pricing, and selecting the best model for your needs. The course also delves into effective methods for Zero, One, and Few Shot Prompting, offering a comprehensive learning journey. We then explore the nuances of the Vertex AI Python LLM API, shedding light on parameters essential for fine-tuning models to peak performance. You'll learn about everything from token limits to temperature settings and sophisticated stop sequences in great detail. A highlight of this course is the practical labs, where students can create various tools, such as an advanced Customer Service Chatbot and a state-of-the-art Translation and Summarization AI Bot. These labs go beyond coding, applying theoretical knowledge to tangible, real-world applications. The course also ventures into the captivating area of prompt engineering. Students will learn to craft effective prompts for tasks like summarization and extraction. In addition, you'll explore text embeddings, learning about context injection in prompts and boosting model accuracy with similarity searches! By the end of the course, students will have the skills to customize their models in the Google Cloud Console, tailoring them to their specific objectives. Who Should Enroll? This course is ideal for AI enthusiasts looking to broaden their knowledge, developers who want to integrate advanced language models into their projects seamlessly, and anyone fascinated by the wonders of Google Cloud and Python in AI. Your Future Is Here! Begin a journey that combines deep theoretical knowledge with practical expertise. With its expert-led instruction and structured modules, this course is a guiding light for those passionate about the wonders of language models. Decide today and shape your future.

ChatGPT and Generative AI has disrupted the world, and is here to remain. Now you have the choice of adopting and mastering it, or falling behind. This course will help you become a prompt wizard, and use ChatGPT, Gemini (previously Bard), and other Large Language Models to 20X your productivity, boost your career, elevate your creativity and so much more. Unlike many other courses out there, this course is crafted by an AI and LLM expert, and focuses on building your intuition about Large Language Models, and providing you the right tools to use Large Language Models like ChatGPT, GPT 4 and Gemini to achieve your goals in a fast moving industry. The objective is not to give you a fish (prompt) and only feed you for a day, but it is to show you how to fish (prompt / work with LLMs) to feed you for a lifetime! We cover the following topics - Intuition about Large Language Models like ChatGPT and Gemini: how these models work, how they are built, and how to understand their functioning for existing and future LLMs - this will allow you to work with any model out there or to come! - Practical usage of ChatGPT and Gemini: learn the functionalities of ChatGPT and Gemini, the differences between them, when to use which, and how to quickly master these powerful tools - Fundamentals of Prompt Engineering: learn the basic toolbox to use prompts to control and supercharge the power of ChatGPT and other LLMs - Prompt patterns to help you achieve more faster: learn the most important prompt patterns to help you achieve your objectives, whether it is summarizing text, generating an essay, building a blogpost, writing an ebook, building a course, crafting a newsletter, building a sales funnel and more - Access ChatGPT everywhere: the best Chrome extensions to access the power of ChatGPT, ChatGPT, and Gemini on all web pages and across your different needs (e.g. twitter replies, email replies, essay writing, web page summarization) - Augment ChatGPT to get more value: what plugins are, how they work, when to use them, and how to use them (e.g. to allow ChatGPT to browse the internet, use links, do arithmetics, plan trips) December 2023 Update: Advanced Prompt Engineering: learn advanced techniques to achieve more with LLMs (ChatGPT, Gemini..) including unlocking new interactions with your LLM, automation, advanced reasoning and problem solving, Techniques to deal with long tasks: such as summarizing very long text, writing a blogpost, or generating a book Open AI APIs & Playground: including building chatbots, and the new assistant functionalities of retrieval and function calling - all with detailed walkthroughs and examples to quickly get you started! Are you ready to embrace the new tech & productivity revolution? Now is your time to get ahead of the crowd! See you on the other side!

In this course, we will explore some techniques and methods that can help you improve the performance of your LLM model beyond traditional fine tuning methods. You should purchase this course if you are a business leader or a developer who is interested in fine tuning your LLM model. These techniques and methods can help you overcome some of the limitations and challenges of fine tuning by enhancing the quality and quantity of your data, reducing the mismatch and inconsistency of your data, reducing the complexity and size of your LLM model, and improving the efficiency and speed of your LLM model. The main topics that we will cover in this course are: - Section 1: How to use data augmentation techniques to increase the quantity and diversity of your data for fine tuning your LLM model - Section 2: How to use domain adaptation techniques to reduce the mismatch and inconsistency of your data for fine tuning your LLM model - Section 3: How to use model pruning techniques to reduce the complexity and size of your LLM model after fine tuning it - Section 4: How to use model distillation techniques to improve the efficiency and speed of your LLM model after fine tuning it By the end of this course, you will be able to: - Explain the importance and benefits of improving the performance of your LLM model beyond traditional fine tuning methods - Identify and apply the data augmentation techniques that can increase the quantity and diversity of your data for fine tuning your LLM model - Identify and apply the domain adaptation techniques that can reduce the mismatch and inconsistency of your data for fine tuning your LLM model - Identify and apply the model pruning techniques that can reduce the complexity and size of your LLM model after fine tuning it - Identify and apply the model distillation techniques that can improve the efficiency and speed of your LLM model after fine tuning it This course is designed for anyone who is interested in learning how to improve the performance of their LLM models beyond traditional fine tuning methods. You should have some basic knowledge of natural language processing, deep learning, and Python programming. I hope you are excited to join me in this course.