Foundation of AI GenAI and Prompt Engineering

Machine learning, a branch of artificial intelligence, learns patterns from data to make predictions without explicit programming. It powers spam filters, recommendations, fraud detection, and autonomous cars.

Collect high quality data, prepare it through cleaning and transformation, then extract meaningful features, select and train the right model, and evaluate with metrics before deployment and monitoring.

Define a model as a mathematical representation of real-world processes learned from data using algorithms and training on features, to generate predictions and evaluate performance with metrics.

Discover how past observations form training data with features like weather, soil, fertilizer, and water to predict yield using a model trained on data and evaluated on a test set.

Learn the main machine learning types: supervised learning with labels for regression and classification, unsupervised learning with unlabeled data and clustering, plus reinforcement learning basics and real world examples.

Explore fundamentals of artificial neural networks by comparing them to the brain’s 86 billion neurons, where signals flow between neurons to drive decision making, and learn how to construct ANNs.

Explore the perceptron, a single neuron, showing binary classification with weighted sums and bias, and see forward and backward propagation through input, hidden, and output layers with activation functions.

Examine fully connected neural networks where every layer's outputs feed every neuron in the next layer, and contrast with partially connected designs in image-based digit recognition.

Explore how convolutional neural networks use convolutional and pooling layers to extract edges and textures, reducing parameters for image classification and object detection.

Understand how recurrent neural networks use a memory-equipped hidden layer to handle sequences and enable next-word prediction, while transformers address training limits.

Transformers drive today’s deep learning, powered by vast data volumes, cloud persistence, and labeled datasets; GPUs and frameworks like Keras and PyTorch enable non-experts to build models.

Trace the evolution of generative AI from foundations to transformers and multimodal systems, then define gen AI and its focus on creating new content with deep learning.

Create new content across text, code, and media with Gen AI. Write articles, generate summaries, create analysis reports, translate, design visuals, and code.

Distinguish machine learning from generative AI by framing tasks as prediction versus creation, with examples like loan default, face generation, and music composition.

Generative AI hinges on data and transformer advances, with data explosion powering models like GPT-4 and DALL-E to transform healthcare, finance, entertainment, and productivity, automating routine tasks and boosting creativity.

Explore how generative AI transforms business productivity with automation, conversational search, content creation, and intelligent insights across healthcare, finance, retail, education, and more.

Explore OpenAI's evolution from a non-profit to a capped-profit organization, and compare GPT models—from GPT-1 to GPT-4—along with ChatGPT, Dall-E, and the OpenAI API.

Explore the landscape of AI models from Microsoft, OpenAI, Google, and others on Azure, including GPT four turbo, Copilot, Dall-E, and Gemini, and examine responsible AI challenges.

Explore prompt engineering for large language models: craft clear, role-based prompts with examples and constraints to elicit accurate, context-rich responses across text, voice, and multi-modal inputs.

Discover how to engineer prompts by attaching ai personas, giving examples, enforcing constraints and formats, and breaking tasks into steps for clear, structured responses.