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Have you used Stable Diffusion and ComfyUI? Do you want to go the next step and step up to consistently producing professional results? If so, then this course is for you! This course is designed for professionals who already have some experience with text-to-image synthesis and want to take their skills to the next level. You will learn how to master Stable Diffusion, the state-of-the-art latent text-to-image diffusion model that can generate photo-realistic images given any text input. You will also learn how to master SDXL, the latest and most advanced version of Stable Diffusion, which can handle challenging concepts and produce images of high quality in virtually any art style. And you will learn how to master ComfyUI, the robust and modular Stable Diffusion GUI and backend that enables you to design and execute advanced Stable Diffusion pipelines using a graph and nodes-based interface. System Requirements A windows PC with a powerful GPU capable of running Stable Diffusion. This is NOT a beginner course and students with entry level hardware will struggle to make use of the resources and exercises. At the very least 8GB of VRAM is recommended for the GPU, more is highly advisable. By taking this course, you will also be able to fine-tune the parameters and the workflows to your needs and preferences, as well as write creative and high-quality prompts that can produce amazing results. You will also gain a deeper understanding of the underlying principles and techniques of text-to-image synthesis, latent diffusion models, and image refinement techniques. This course is not for beginners who need introductory learning. This course is for professionals who want to master Stable Diffusion, SDXL and ComfyUI and achieve polished, professional outcomes. If you are ready to take your text-to-image synthesis skills to the next level, then enroll in this course today! Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. It is based on the idea of reversing the diffusion process, which gradually transforms an image into random noise. By applying the reverse steps with a neural network conditioned on text, Stable Diffusion can recover the original image from the noise. SDXL is the latest and most advanced version of Stable Diffusion, which leverages a larger and more powerful UNet backbone with more attention blocks and a larger cross-attention context. SDXL can generate images of high quality in virtually any art style and is the best open model for photorealism. It can also handle challenging concepts such as hands, text, and spatial arrangements. ComfyUI supports SD1.x, SD2.x and SDXL models, as well as standalone VAEs and CLIP models. ComfyUI also offers many features such as embeddings, textual inversion, Loras, hypernetworks, loading and saving workflows, and image control. The goal of this course is to help you achieve polished, professional outcomes with Stable Diffusion, SDXL and ComfyUI. You will learn how to use these tools effectively and efficiently, as well as how to fine-tune them to the demands of your work and your preferences. By the end of this course, you will be able to get the most out of the efficiency and flexibility of ComfyUI to produce reliably consistent results that begin to match the best in the industry.

About this Course Do you want to take full advantage of the new Bing AI? Then you are at the right place! In this course, you will learn completely how to use the new Bing AI for your business, learning, teaching, research, overall content creation journey etc. Introducing the new Bing search, your AI-powered copilot for the web. It is powered by the same technology behind ChatGPT and draws on the deep knowledge base behind Bing search. The combination means you'll get reliable, up-to-date results, and complete, cited answers to your questions! You'll also get to chat, use voice search, and see suggestions for additional information. It can even build on your ideas to write drafts for you to consider. The new Bing is an experience that combines the Microsoft search engine with a custom version of the ChatGPT bot from OpenAI to provide more human conversational answers rather than a list of links on a page that may or may not contain the correct information. In this course, you will learn: - How to get access to the chatbot - Planning vacations - Linking and explaining a website - Product details and reviews - Generating YouTube ideas - Writing CVs and Job application Letters - Solving complex math problem - Story writing - Writing ebooks - Writing computer codes - Writing blog posts - Writing meal plan - Improve writing Who can take this course? - This course is for those, who want to improve their business by getting beneficial ideas - This course is for those who want to improve any skill, for example electrical, designing, media, medical, physics, chemistry, engineering, programming, software engineering, etc. - This course is for those, who want to make money online through different skills, for example, to make money, finding SEO keywords, writing content for blogs, Facebook videos description, youtube video descriptions, etc. - This course is also for those who want to improve their programming because the new bing AI can write code for you in any programming language like Python, PHP, Java, javascript, etc. About Instructor: Engr. Dr. A. S. Abdurrasheed is a university lecturer, a YouTuber, and a presentation coach with a Ph.D. in Civil Engineering from Universiti Teknologi PETRONAS, Malaysia. He is the founder of EPIC Mentorship and the EPIC Digital Academy. A multiple award-winning professional with an international award of merit from the republic of Croatia, and gold medal awards from the International Invention, Innovation, and Technology Exhibition (ITEX) and the Malaysian Technology Expo (MTE). In 2022, he received the triple crown award from toastmasters international. A UTP brand ambassador with a gold lapel pin award. He was recently featured as a speaker at the Bizmaker 2022 – Moving to Mastery, Career, and Leadership Aid Program (CLAP 2022) sponsored by IEEE young professionals. Additionally, he participated as an honorary invited speaker at the MyPSA metamorphosis 4.0. He equally participated as an Invited speaker at the Micropollutant Research Centre (MPRC) of Universiti Tun Hussein Onn Malaysia (UTHM).

Welcome to the comprehensive course on practical applications of deep learning with Keras! In this course, you will embark on an exciting journey through various projects aimed at developing practical skills in deep learning and neural networks using the Keras framework. Whether you're a beginner looking to get started with deep learning or an experienced practitioner seeking to enhance your skills, this course offers something for everyone. Throughout this course, you will dive into hands-on projects covering a wide range of topics, including building chatbots, sentiment analysis using recurrent neural networks (RNNs), image classification, and advanced face recognition computer vision applications. Each project is carefully designed to provide you with practical experience and insights into real-world applications of deep learning. By the end of this course, you will have gained valuable experience in implementing deep learning models, understanding their underlying principles, and applying them to solve complex tasks. Whether you're interested in natural language processing, computer vision, or any other domain, the skills you acquire in this course will be invaluable in your journey as a deep learning practitioner. Get ready to unlock the full potential of deep learning with Keras and take your skills to the next level! Section 1: Building A Chatbot with keras In this section, students will embark on a practical journey of constructing a chatbot using Keras. They will begin with an introduction to the project's objectives, followed by an exploration of foundational concepts such as the Bag of Words (BoW) model, Count Vectorizer, and techniques for handling text data. Through a series of progressive lectures, students will delve into preprocessing steps, feature limitation strategies, and essential text processing elements like stop words and stemming. Section 2: Project On Keras: Sentimental Analysis Using RNN In the second section, students will transition to another project focusing on sentiment analysis with Recurrent Neural Networks (RNNs) using Keras. They will be introduced to Google Colab for collaborative work and IMBD dataset for sentiment analysis. The section will cover topics such as padding sequences, basic and complex LSTM models, and training procedures, enabling students to gain practical experience in sentiment analysis. Section 3: Project On Keras - Image Classification Continuing the journey, students will move to image classification projects in this section. They will learn to set up Google Colab, download datasets, and employ pretrained models for image classification tasks. Topics covered will include intermediate layer visualization, model creation, image augmentation, and model evaluation techniques. Section 4: Project On Keras - Creating An Advanced Face Recognition Computer Vision App In the final section, students will engage in creating an advanced face recognition application using computer vision techniques with Keras. They will explore Convolutional Neural Networks (CNNs) for image processing, face detection using MTCNN, and building a classifier for face recognition. This section will culminate in a comprehensive understanding of implementing deep learning models for real-world applications.

This course covers what Generative AI is and its applications to a variety of tasks in the legal field. The course will cover ethics and privacy, basic prompt engineering strategies for legal use cases, and how to leverage Generative AI to augment your legal expertise. This is a rapidly advancing area and a primary goal of this course is to prepare everyone for the pace of change that is taking place and to develop a mindset of continuous curiosity that will enable lawyers to adapt along with the technology

Generative AI has gone mainstream. Boost your career with a course that introduces cutting-edge, in-demand generative AI skills tailored to the needs of data scientists. This course is suitable for current and aspiring data scientists, analysts, and engineers. The course explores real-world data science problems faced by data scientists across industries using data generation, data augmentation, and feature engineering. Gain skills that you can immediately apply as you implement generative models and AI techniques to solve these real-world problems. Then, learn how to use generative AI to speed up data visualization, model building, and insightful data analysis. You’ll also learn about key ethical considerations surrounding generative AI and data that are of concern to decision makers across industries. Showcase your new generative AI skills with a hands-on data augmentation and feature engineering project that you can apply in your real life. Then take a final quiz to earn a certificate. You’ll be able to share your project and certificate with your current or future employers.

In this course, you will learn about the four phases of the disaster management cycle: mitigation, preparation, response, and recovery. In this course, you will look at two case studies. In the first, you will use computer vision to analyze satellite imagery from Hurricane Harvey in 2017 to identify damage in affected areas. In the second, you will use natural language processing techniques to examine trends in requests for assistance following the 2010 Haiti earthquake.

Generative AI is transforming the field of software development, making it an essential skill for developers to have in their arsenal. This IBM course, Generative AI for Software Developers, is designed to give you a comprehensive understanding of how generative AI techniques can be applied to improve software development processes. This course is designed to provide the necessary skills and knowledge to use AI-based tools and algorithms to improve the efficiency of software development processes. Through a combination of videos, hands-on exercises, demos, and projects, you will learn how to use generative AI models to automate various aspects of software development, including code generation, design and architecture, error detection, and optimization. This course will also help you understand the ethical considerations that must be followed to use AI to its full potential. Enroll in the course today to get the most out of AI for software development and boost your career!

Leveraging Generative AI for Social Impact Organizations focuses on the issue of resource scarcity commonly faced by social impact organizations and how generative AI tools can help free up limited staff capacity from routine administrative tasks. Social impact organizations often find themselves overwhelmed by repeatedly responding to common inquiries, data management and documentation needs, and other basic functions that limit their ability to fully devote time to core programming and service delivery. In this course, you'll learn concrete examples of how AI could improve efficiency in areas like automatically answering frequently asked questions, converting information requests into readable reports, or drafting, designing, and distributing communications materials. Gain insight into key opportunities to leverage AI to expand your organization's bandwidth for mission-critical activities. The course also explores the limitations and ethical considerations of relying on generative AI versus human judgment and nuanced interactions in social programming.

Ready for interactive learning on how to develop apps and chatbots for different use cases using generative AI? This course provides the opportunity to work on instructor-led projects that provide step-by-step instructions for building generative AI applications. You will use Python, as well as related libraries such as Flask and Gradio, and frameworks such as Langchain. During the course, you will work on practical projects to build chatbots and applications using popular large language models (LLMs) such as GPT-3 and Llama 2, hosted on platforms such as IBM watsonx and Hugging Face. You will also be introduced to search augmentation generation (RAG), a technique that extends the capabilities of LLMs by incorporating external information beyond the training data. This course will also teach you how to build chatbots and voice-enabled applications using IBM Watson® Speech Libraries for Embed. You will use Python to develop these projects, making a basic understanding of the language necessary. While knowledge of HTML, CSS, and JavaScript can be helpful, it is not a prerequisite. The course includes supporting videos and literature to help develop a basic understanding of the models, frameworks and technologies used in projects.

This is an abbreviated summary of our comprehensive Generativity Navigator for Leaders program. Start your journey with this accelerated 4-hour course. If you need to go deeper, your progress will transfer to our longer program. Created by the CEO of Coursera, this course is your quick guide to unlocking the transformative power of GenAI for your organization. It includes hands-on labs with access to Google Gemini Pro in a secure, private environment. In these labs, you’ll not only learn how to use GenAI, but also how to apply it to develop a GenAI strategy, identify specific opportunities to drive customer value, and increase productivity. Learn from top minds in the field, including Andrew Ng, global AI expert and co-founder of Coursera; Clara Shih, CEO of Salesforce AI; and Hayden Brown, CEO of Upwork. With these expert insights, updated quarterly, you’ll always be on the cutting edge of GenAI developments. Embrace the future of AI with confidence. Start your AI journey today.

Learn about two different approaches to transforming raw data into data ready for analysis. One approach is the Extract, Transform, Load (ETL) process. The other, opposite approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data maps. ELT processes apply to data lakes, where data is transformed on demand by the requesting/calling application. In this course, you will learn about the various tools and techniques used in ETL and Data Pipelines. Both ETL and ELT extract data from source systems, move the data through a data pipeline, and store it in destination systems. In this course, you will learn how ELT and ETL data processing differ and identify use cases for both techniques. You will identify the techniques and tools used to extract data, combine the extracted data logically or physically, and load the data into data warehouses. You will also determine what transformations should be applied to the source data to make it authoritative, contextual, and accessible to data users. You will be able to describe some of the many techniques for loading data into a destination system, checking data quality, monitoring for loading failures, and using recovery mechanisms in the event of a failure. By the end of this course, you will also know how to use Apache Airflow to build data pipelines and be aware of the benefits of using this approach. You will also learn how to use Apache Kafka to build streaming data pipelines, as well as the main components of Kafka, which include brokers, topics, partitions, replications, producers, and consumers. Finally, you will complete a capstone project that will allow you to demonstrate the skills you have learned in each module.

This is a self-paced lab that takes place in the Google Cloud console. This lab will give you introductory hands-on experience with generative AI in Google Cloud.