Part 1, overview and introduction, of a 3-part introductory series on using Machine Learning and Artificial Intelligence with Python. We will discuss basic machine learning concepts with code examples, introducing concepts such as ensemble methods (random forests, gradient boosting), multi-level perceptrons (simple neural networks), and K-means clustering using the Python scikit-learn framework. Subsequent talks in this series will cover deep learning frameworks such as TensorFlow and PyTorch for natural language processing and computer vision tasks.