This lecture provides an introduction to deep machine learning in the domain of weather, climate and air quality research. The atmospheric is a highly complex and dynamical system in which many physical, chemical and biological processes interact on a wide range of spatial and temporal scales. As a consequence, atmospheric data has some properties that differ from other machine learning applications and established ML methods may not always work well when applied to atmospheric data. The lecture is structured into four parts. First, we discuss some general properties of the atmosphere and atmospheric data. Next, a brief summary of atmospheric statistics and evaluation metrics is given. In the third part, we will learn some machine learning fundamentals and compare machine learning models and numerical (weather) models. The fourth part provides several examples of machine learning applications in the weather and climate domain with a focus on weather forecasting. Useful readings mentioned in the presentation below. Generative adversarial network: https://towardsdatascience.com/generative-adversarial-networks-explained-34472718707a Tranformers: https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452 Autoencoders guide: https://www.v7las.com/blog/autoencoders-guide