All videos available: https://www.youtube.com/channel/UCrJhliKNQ8g0qoE_zvL8eVgLessons learned: pick up swag on day one. vendors run out.take business cards with you and keep them on youNot your actual business cards unless you are representing your company.Cards that have your social media, github account, blog, or podcast or whatever on them.3x3 stickers are too big. 2x2 plenty big enoughlightening talks are awesome, because they are a lot of ranges of speaking experiencewill definitely do that againtry to go to the talks that are important to you, but don’t over stress about it, since they are taped. However, it would be lame if all the rooms were empty, so don’t everybody ditch.lastly: everyone knows Michael. Michael #2: How to Create Your First Python 3.6 AWS Lambda FunctionTutorial from Full Stack PythonWalks you through creating an accountSelect your Python version (3.6, yes!)def lambda_handler(event, context): … # write this function, done!Set and read environment variables (could be connection strings and API keys)Brian #3: How to Publish Your Package on PYPIjetbrains article structure of the packageoops. doesn't include src, see https://pythonbytes.fm/22decent discussion of a the contents of the setup.py file (but interestingly absent is an example setup.py file)good discussion of .pypirc file and links to the test and production PyPiexample of using twine to push to PyPIoverall: good discussion, but you'll still need a decent example.Michael #4: Coconut: Simple, elegant, Pythonic functional programmingCoconut is a functional programming language that compiles to Python. Since all valid Python is valid Coconut, using Coconut will only extend and enhance what you're already capable of in Python.pip install coconutSome of Coconut’s major features include built-in, syntactic support for:Pattern-matching,Algebraic data-types,Tail call optimization,Partial application,Better lambdas,Parallelization primitives, andA whole lot more, all of which can be found in Coconut’s detailed documentation.Talk Python episode coming in a weekBrian #5: Choose a licenceMIT : simple and permissiveApache 2.0 : something extra about patents.GPL v3 : this is the contagious one that requires derivitive work to also be GPL v3Nice list with overviews of what they all mean with color coded bullet points: https://choosealicense.com/licenses/Michael #6: Python for Scientists and EngineersTable of contents:Beginners Start Here:Create a Word Counter in PythonAn introduction to Numpy and MatplotlibIntroduction to Pandas with Practical Examples (New)Main BookImage and Video Processing in PythonData Analysis with PandasAudio and Digital Signal Processing (DSP)Control Your Raspberry Pi From Your Phone / TabletMachine Learning SectionMachine Learning with an Amazon like Recommendation EngineMachine Learning For Complete Beginners: Learn how to predict how many Titanic survivors using machine learning. No previous knowledge needed!Cross Validation and Model Selection: In which we look at cross validation, and how to choose between different machine learning algorithms. Working with the Iris flower dataset and the Pima diabetes dataset.Natural Language ProcessingIntroduction to NLP and Sentiment AnalysisNatural Language Processing with NTLKIntro to NTLK, Part 2Build a sentiment analysis programSentiment Analysis with TwitterAnalysing the Enron Email Corpus: The Enron Email corpus has half a million files spread over 2.5 GB. When looking at data this size, the question is, where do you even start?Build a Spam Filter using the Enron CorpusIn other news:Python Testing with pytest Beta release and initial feedback is going very well.