Artificial Intelligence Essentials: AI-based Chatbots

Colleagues, the “Artificial Intelligence Essentials: AI-based Chatbots” program will teach you what AI Chatbots are by understanding how they differ from regular chatbots and what are good use cases You have probably come across chatbots when browsing online. You might notice some are a bit more equipped than others. Some of these well-equipped ones might have been an AI-based chatbot. In this course, Artificial Intelligence Essentials: AI-based Chatbots, you will gain the ability to Evaluate AI-based chatbots. First, you will explore what the foundation of AI-based chatbots are and how they perform. Next, you will discover how AI-based chatbots can be used in modern day products by going through different use cases of AI-based chatbots. Finally, you will learn what applications are available to create an AI-based chatbot. By the end of this course, you will have the skills and knowledge of AI-based chatbots and how to use them in your own environment. Training modules include: 1) Evaluating AI Based Chatbots - What Is a Chatbot? Types of Chatbots, Examples of AI Based Chatbots, 2) Understanding AI Based Chatbots - Components of an AI Chatbot, What Is Natural Language Processing?, What Is a Knowledge Base?, Understanding Intent, 3) Describing Applications of AI Based Chatbots - Industry Use of AI Chatbots, Demo: Retail Chatbot, 4) Using Tools to Create an AI based Chatbots - Tools to Built an AI Chatbot, SDK and API for AI Chatbots, Demo: Using ChatGPT with OpenAI API via Code, AI Chatbot Builders, Demo: AI Chatbot with Azure Cognitive Question Answering. 

Enroll today: https://pluralsight.pxf.io/WqO0KX (teams & executives are welcome)

Read the OpenAI ChatGPT 4 - Technical Report. (https://arxiv.org/pdf/2303.08774.pdf) 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to the ChatGPT audiobook on Audible. (https://tinyurl.com/bdfrtyj2) or read the ebook today on Amazon Kindle. (https://tinyurl.com/jfntsyj2) 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)

Prompt Engineering for ChatGPT

ChatGPT and other large language models are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it. This course will teach you how to be an expert user of these generative AI tools. The course will show amazing examples of how you can tap into these generative AI tools' emergent intelligence and reasoning, how you can use them to be more productive day to day, and give you insight into how they work. Large language models respond to instructions and questions posed by users in natural language statements, known as “prompts”. Although large language models will disrupt many fields, most users lack the skills to write effective prompts. Expert users, who understand how to write good prompts, are orders of magnitude more productive and can unlock significantly more creative uses for these tools. This course introduces students to the patterns and approaches for writing effective prompts for large language models. Anyone can take the course and the only required knowledge is basic computer usage skills, such as using a browser and accessing ChatGPT. Students will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. By the end of the course, students will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming. {Coursera}

Enroll today:  Prompt Engineering for ChatGPT

Download your complimentary AI-ML-DL - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

The Promise of Data-Driven Decision Making - From Analytics to Visualization and Beyond (new audiobook & ebook)

Colleagues, “The Promise of Data-Driven Decision Making” is a powerful resource that has the potential to transform the way we approach problem-solving in both our personal and professional lives. By collecting and analyzing data, we can gain valuable insights into the world around us, and use that knowledge to make more informed decisions. Throughout this e-book, we have explored the various aspects of data-based decision making, including the benefits, challenges, and best practices. We have also examined the different tools and techniques that can be used to collect and analyze data, as well as the ethical considerations that must be taken into account.

Highly data-driven firms are three times more likely to report a major improvement in decision making, according to a large decision survey conducted by PWC. However, only one in three CEOs claim that their company is heavily data-driven. It comes up frequently in meetings with corporate leaders that executives have instant access to large volumes of data. We also learn that their personal intuition or gut feeling plays a significant role in their decision-making. How might the art and science of decision-making be combined better? A more efficient use of data and the capacity to draw insights are seen to present potential for enterprises to generate higher value. Analytics may support an organization's growth and innovation, increase productivity, and improve risk management when they are integrated into the culture of decision-making within the company. The use of facts, metrics, and data to inform strategic business decisions that are in line with a company's goals, objectives, and activities is known as data-driven decision-making. Interactive dashboards, work management platforms, and OKR tools are examples of modern analytics tools that assist individuals overcome prejudice and make the best management decisions that are in line with business strategies. Instead of making decisions based on intuition, opinion, or personal experience, it compiles historical data to examine trends and make better decisions for the future in relation to what has previously worked.

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

This book is part of the “Data-Driven Organizations” series.

Much success, Genesys Digital (share with your team) 

“Implementing Data Science Methodology” (new ebook & audiobook)

Colleagues, the new ebook entitled “Implementing Data Science Methodology … From Data Wrangling to Data Viz and Everything in Between” will enable you to lead data science initiatives within your organization. They allow organizations to make informed decisions based on data, which can lead to better results and increased competitiveness. Data-driven methodologies also help organizations to identify areas for improvement and to make more accurate predictions about future outcomes and improve the accuracy of decision-making. By analyzing large amounts of data, organizations can identify patterns and trends that would not be apparent otherwise. This allows them to make more informed decisions, reducing the risk of making decisions based on inaccurate or incomplete information. The implementation of data-driven methodologies is critical for organizations looking to remain competitive in today’s data-driven world. By leveraging data to make informed decisions, organizations can improve their performance, reduce risks, and provide better experiences for their customers. It is important for organizations to invest in the resources and processes needed to implement these methodologies effectively, and to stay up-to-date with the latest trends and technologies in data science. Key topics addressed include: 1) Data-Driven Methodologies, 2) The Role of the Data Scientist, 3) Stakeholders and Buy-in, 4) Key Concepts of Data-Driven Methodology, 5) Data-Driven Lifecycle, 6) Data-driven Roadmapping, 6) Planning and Implementing a Data-Driven Strategy, 7) Data-Driven Predictive Modeling, 8) Business Transformation, and 9) Examples of Data-Driven Organizations.

Read this new ebook today on Amazon Kindle: https://www.amazon.com/dp/B0BWVHSM4N 

Access & listen on Amazon Audible: https://tinyurl.com/mt3xs4ys 

This book is part of the “Data-Driven Organizations” series: https://tinyurl.com/4j49m4zm 

Much success, Genesys Digital (share with your team) 

The Promise of Data-Driven Decision Making - From Analytics to Visualization and Beyond (ebook)

Colleagues, “The Promise of Data-Driven Decision Making” is a powerful resource that has the potential to transform the way we approach problem-solving in both our personal and professional lives. By collecting and analyzing data, we can gain valuable insights into the world around us, and use that knowledge to make more informed decisions. Throughout this e-book, we have explored the various aspects of data-based decision making, including the benefits, challenges, and best practices. We have also examined the different tools and techniques that can be used to collect and analyze data, as well as the ethical considerations that must be taken into account.

Highly data-driven firms are three times more likely to report a major improvement in decision making, according to a large decision survey conducted by PWC. However, only one in three CEOs claim that their company is heavily data-driven. It comes up frequently in meetings with corporate leaders that executives have instant access to large volumes of data. We also learn that their personal intuition or gut feeling plays a significant role in their decision-making. How might the art and science of decision-making be combined better? A more efficient use of data and the capacity to draw insights are seen to present potential for enterprises to generate higher value. Analytics may support an organization's growth and innovation, increase productivity, and improve risk management when they are integrated into the culture of decision-making within the company. The use of facts, metrics, and data to inform strategic business decisions that are in line with a company's goals, objectives, and activities is known as data-driven decision-making. Interactive dashboards, work management platforms, and OKR tools are examples of modern analytics tools that assist individuals overcome prejudice and make the best management decisions that are in line with business strategies. Instead of making decisions based on intuition, opinion, or personal experience, it compiles historical data to examine trends and make better decisions for the future in relation to what has previously worked.

Access this new ebook today on Amazon Kindle.

And download your free Data Science - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

Implementing Data Science Methodology (ebook)

Colleagues, the new ebook entitled “Implementing Data Science Methodology … From Data Wrangling to Data Viz and Everything in Between” will enable you to lead data science initiatives within your organization. They allow organizations to make informed decisions based on data, which can lead to better results and increased competitiveness. Data-driven methodologies also help organizations to identify areas for improvement and to make more accurate predictions about future outcomes and improve the accuracy of decision-making. By analyzing large amounts of data, organizations can identify patterns and trends that would not be apparent otherwise. This allows them to make more informed decisions, reducing the risk of making decisions based on inaccurate or incomplete information. The implementation of data-driven methodologies is critical for organizations looking to remain competitive in today’s data-driven world. By leveraging data to make informed decisions, organizations can improve their performance, reduce risks, and provide better experiences for their customers. It is important for organizations to invest in the resources and processes needed to implement these methodologies effectively, and to stay up-to-date with the latest trends and technologies in data science. Key topics addressed include: 1) Data-Driven Methodologies, 2) The Role of the Data Scientist, 3) Stakeholders and Buy-in, 4) Key Concepts of Data-Driven Methodology, 5) Data-Driven Lifecycle, 6) Data-driven Roadmapping, 6) Planning and Implementing a Data-Driven Strategy, 7) Data-Driven Predictive Modeling, 8) Business Transformation, and 9) Examples of Data-Driven Organizations.

Access this new ebook on the Amazon Kindle today.

And download your free Data Science - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

AI Software Engineer - ChatGPT, Bard … and Beyond (ebook)

Colleagues, this new ebook “AI Software Engineer - ChatGPT, Bard … and Beyond” aims to help Software Engineers and Developers capture their ideal job offer and manage their medium-to-long-term career growth in the global Artificial Intelligence (AI) arena. First, we will look at the global market overview for AI Globally. The artificial intelligence (AI) market size was valued at around $136.55 billion in 2022 (Grand View Research, 2022). Also, it was projected to have a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. if you're interested in AI, you should be familiar with and have expertise with at least one of the following programming languages: Python, C/C++, MATLAB. According to Indeed, salaries for artificial intelligence professionals often range from $99,568 for a full-stack developer to $142,318 for a data scientist. Glassdoor reports the annual average base salary for artificial intelligence professionals in the US is $120,048. Based on a Talent.com report, the average artificial intelligence salary is $143,054 annually. Entry positions start at $115,000, and experienced employees can earn up to $200,000 yearly. 

Access this new ebook on the Amazon Kindle today.

And download your free AI-ML-DL - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

ChatGPT … ChatGPT, Bard and Beyond

Colleagues, Generative conversational AI has the potential to improve accessibility for people with disabilities and those who struggle with language barriers, as AI models can be trained to understand and respond to a wide range of languages and dialects. Generative conversational AI represents a major shift in how we interact with technology and has the potential to improve many aspects of our lives, from customer service and support to healthcare and accessibility. Check out this new ebook!

ChatGPT is a specific implementation of Generative conversational AI technology developed by OpenAI. It is a large language model trained on vast text data, allowing it to generate human-like responses to text inputs. In the context of conversational AI, ChatGPT can be used to build chatbots, virtual assistants, and other applications that require the ability to generate text in real time. The model's size and training data allow it to develop highly relevant and human-like text, making it well-suited for various applications. As a state-of-the-art Generative conversational AI, ChatGPT is the perfect tool for organizations looking to step up their communication game. Whether you want to improve interactions with customers, employees, or other stakeholders, ChatGPT makes it easy. Want to see just how much you can achieve with this powerful tool? 

Access this new ebook from the Amazon Kindle Store today: https://tinyurl.com/5yfwvh8a 

And download your free AI-ML-DL - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)

OpenAI’s ChatGPT - Generative AI for Such a Time As This

Colleagues, according to CNET’s article (December 2023) “There's a new AI bot in town: ChatGPT, and even if you're not into artificial intelligence, you'd better pay attention.” Moreover, “It's a big deal. The tool seems pretty knowledgeable in areas where there's good training data for it to learn from. It's not omniscient or smart enough to replace all humans yet, but it can be creative, and its answers can sound downright authoritative. A few days after its launch, more than a million people were trying out ChatGPT.” ChatGPT can write blog posts, articles and even create computer programs. This chatbot is still in its infancy. However, as ChatGPT scans more Internet content and expands its knowledge base, the applications for consumers and professionals alike appear almost limitless.

With Generative AI’s astounding potential it is time for software developers and engineers to get training and certified. Here are our top 4 training program recommendations for 2023. First is Contact Center AI: Conversational Design Fundamentals you will learn to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will be introduced to CCAI and its three pillars (Dialogflow, Agent Assist, and Insights), and the concepts behind conversational experiences and how the study of them influences the design of your virtual agent. After taking this course you will be prepared to take your virtual agent design to the next level of intelligent conversation. This is a beginner course, intended for learners with the following types of roles: Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows. Citizen developers: Creates new business applications for consumption by others using high level development and runtime environments. Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API. Operations specialists: Monitors system operations and troubleshoots problems. Installs, supports, and maintains network and system tools. Second is Build Basic Generative Adversarial Networks (GANs). Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research. Next is Machine Vision, GANs, and Deep Reinforcement Learning. Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. And fourth, Apply Generative Adversarial Networks (GANs). Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Enroll today (teams & execs welcome): 

• Contact Center AI: Conversational Design Fundamentals

• Build Basic Generative Adversarial Networks (GANs)

• Machine Vision, GANs, and Deep Reinforcement Learning

• Apply Generative Adversarial Networks (GANs)

 

And sign-up for free access to ChatGPT.

 

Download your complimentary AI-ML-DL - Career Transformation Guide.

 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team) 

Graphic source: TechBuild

Machine Learning in Trading and Finance

Colleagues, the Machine Learning in Trading and Finance program from the New York Institute of Finance and Google Cloud will equip you in Quantitative trading, pairs trading, and momentum trading. You will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. You should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas.By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading.  This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

Enroll today (teams & execs are welcome):  https://tinyurl.com/3zxdkb6m 

And download your free Finance, Accounting & Banking - Career Transformation Guide.

Much career success, Lawrence E. Wilson - Financial Certification Academy (share with your team)