Generative AI for Beginners AI, ML & LLM Fundamentals

Discover how machine learning builds algorithms from historical data by recognizing patterns and predicting outputs, contrasting legacy programming with data-driven input-output learning.

Choose the right algorithm for your data to build a model, and learn linear and logistic regression, naive Bayes, decision trees, support vector machine, artificial neural network, and clustering.

Explore how neural networks mimic the brain by processing through layered nodes with weights, biases, and activation functions to decide, and when to use them over machine learning.

Learn about the perceptron, a single neuron from 1958. Understand how weighted inputs, a bias, and a step activation function implement binary classification.

Explore fully connected dense neural networks, where every neuron connects to the next layer, revealing heavy compute and 625-input image examples that make them impractical for images.

Convolutional neural networks extract image features with 3x3 kernels, ReLU, and pooling to reduce inputs for a classifier, enabling image classification, object detection, and OCR.

Autoencoders use unsupervised learning to compress data into a latent space via an encoder and decoder, enabling reconstruction and noise reduction in image and video processing.

This lecture explains diffusion models that add noise to images and reverse denoise to recreate high-quality images, highlighting text-to-image, video, and audio with transformers.

Learn how language models construct a dictionary of tokens from words, assign ids to each token, and grow vocabulary as training data increases.

Learn how transformers use token and positional embeddings with sine and cosine encoding, then apply encoder self-attention and multi self-attention to guide the decoder in predicting the next word.

Learn how foundation models are generic and how fine tuning with private, proprietary data creates organization-specific custom models.

Trace OpenAI’s evolution from the 2015 founding by Elon Musk and Sam Altman to a capped-profit model, culminating in ChatGPT’s 2022 breakthrough and early OpenAI API developers using GPT-2.

Apply Microsoft’s six responsible AI principles—fairness, reliability, inclusiveness, transparency, accountability, and liability—while prioritizing privacy, security, HIPAA, GDPR, and PII compliance, thorough testing, resilience, and enhancing human capabilities.