An Introduction to Artificial Intelligence & Machine Learning

using python & common python libraries to explore & analyze data: statistics, probabiliy, Percentiles, Moments, Covariance, Correlation, Conditional Probability, & Bayes' Theorem. Also, an introduction to tensorflow.

Linear Regressions, Decision Trees, K-Means clustering, Ensemble learning (bagging & boosting), & building models.

Intro to Neural Networks

What they are, & how to create & fine-tune them.

Natural Language Processing (NLP)

Focusing on language and words: word embeddings, transformers, LLMs, prompt engineering, Retrieval Augmented Generation (RAG).

Computer Vision

Processing images, using convolutional neural networks (CNNs).

Deploying models to the world