Generative AI for Leaders and Mobilizers
- 2 Courses | 1h 40m
Learn about Generative AI and how to use it to catalyze the transformation of business.
COURSES INCLUDED
Getting Started with Prompt Engineering
Generative artificial intelligence (GenAI) can create new content, such as text, images, and music. It is powered by machine learning (ML) models that have been trained on massive datasets of existing content. Prompt engineering is the process of designing and crafting prompts that guide generative AI models to produce the desired output. You will start this course by learning how you can leverage prompt engineering to improve your day-to-day and work-related tasks. Next, you will see examples of prompting in action with external generative AI chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat. As several of these tools may not be supported on many corporate devices, you will not be expected to create accounts on those platforms, but you will be able to apply the learnings and principles to any corporate conversational AI chatbot in similar ways.
15 videos |
2h 1m
Assessment
Badge
Exploring the OpenAI Playground
The OpenAI Playground is a web-based tool that lets you experiment with large language models (LLMs) to generate text, translate languages, write creative content, and answer your questions in an informative way. With the Playground, you can input text prompts and receive real-time outputs, and you can adjust hyperparameters to control the creativity, randomness, length, and repetition of the model responses. In this course, you will begin by creating an account to use the OpenAI Playground and you will learn how you are billed for its usage. Next, you will explore the different chat modes and models and work with the hyperparameters that allow you to configure creativity, randomness, repetition, and the length of model responses. You will also use stop sequences, which terminate the output when a specific phrase is reached, as well as the frequency and presence penalty, which penalize repetition of words and topics. Finally, you will learn how to view probabilities in generated text and explore how to use presets to share prompts and prompt parameters with other people.
14 videos |
1h 46m
Assessment
Badge
Exploring Prompt Engineering Techniques
Different types of prompts serve distinct purposes when interacting with language models. Each type enables tailored interactions, from seeking answers and generating code to engaging in creative storytelling or eliciting opinions. In this course, you will learn the four elements of a prompt: context, instruction, input data, and output format. Next, will also explore prompt categories, such as open-ended, close-ended, multi-part, scenario-based, and opinion-based. Finally, you will look at different types of prompts based on the output that they provide. You will use prompts that generate objective facts, abstractive and extractive summaries, classification and sentiment analysis, and answers to questions. You'll tailor prompts to perform grammar and tone checks, ideation and roleplay, and mathematical and logical reasoning.
13 videos |
1h 54m
Assessment
Badge
Case Studies in Prompt Engineering
Data generation prompts instruct language models to generate synthetic data, useful for creating datasets. Code generation prompts are used to produce code snippets or entire programs, aiding developers in coding tasks. Zero-shot prompts challenge models to respond to unfamiliar tasks, relying on their general knowledge. Few-shot prompts provide limited context to guide models in addressing specific tasks, enhancing their adaptability. You will start this course by working with data generation and code generation prompts. You will explore how to use starter code prompts, convert code from one language to another, and prompt models to explain a piece of code. Next, you will see how to leverage generative AI to debug your code and generate complex bits of code with step-by-step instructions. Finally, you will explore techniques to improve prompt performance.
10 videos |
1h 19m
Assessment
Badge
Artificial Intelligence and Machine Learning
This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.
11 videos |
1h 36m
Assessment
Badge
Deep Learning and Neural Networks
Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.
11 videos |
1h 20m
Assessment
Badge
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