Author : Guillaume Pitron P17 : Moins de pollution, mais beaucoup plus d’énérgie dans le même temps. Ce n’est dès lors pas un hasard si l’un de ces éléments fut baptisé prométhium à sa découverte par le chimiste Charles Coryell dans les années 1940 : c’est Grace Marie, son épouse, Read more…
Data Science for Beginners Numpy Basics Matplotlib : Plotting in Python Pandas
Data Pipelines Ingesting real-time data poses a number of challenges: Have to scale in real time Have to deal with data being late Have to deal with bad data coming in real time (duplicates, missing data, etc.) Cloud Pub/Sub Distributed messaging system to handle real-time messaging. Serverless Big Data Pipeline Read more…
Notes for the Google Cloud Platform Big Data and Machine Learning Fundamentals course. https://www.coursera.org/learn/gcp-big-data-ml-fundamentals/home/welcome https://console.cloud.google.com WEEK 1 1. Google Cloud Architecture -2. Abstract away scaling, infrastructure -1. Process, store, and deliver pipelines, ML models, etc. -0. Auth 2. Creating a VM on Compute Engine 2.1 Setting up the VM https://cloud.google.com Read more…
Summary of course topics Supervised learning – labeled data Linear regression Logistic regression Neural networks Support vector machines Unsupervised learning – unlabeled data K-means PCA Anomaly detection Special applications/topics Recommender systems Large scale machine learning Advice on building machine learning systems Bias and variance Regularization What to do next when developing a Read more…
Problem description and pipeline Case study focused around photo OCR Three reasons to do this 1) Look at how a complex system can be put together 2) The idea of a machine learning pipeline What to do next How to do it 3) Some more interesting ideas Applying machine learning to tangible problems Artificial data synthesis What is the photo OCR Read more…
Learning with large datasets This set of notes look at large scale machine learning – how do we deal with big datasets? If you look back at 5-10 year history of machine learning, ML is much better now because we have much more data However, with this increase in data comes Read more…
Recommender systems – introduction Two motivations for talking about recommender systems Important application of ML systems Many technology companies find recommender systems to be absolutely key Think about websites (amazon, Ebay, iTunes genius) Try and recommend new content for you based on passed purchase Substantial part of Amazon’s revenue generation Improvement in Read more…
Anomaly detection – problem motivation Anomaly detection is a reasonably commonly used type of machine learning application Can be thought of as a solution to an unsupervised learning problem But, has aspects of supervised learning What is anomaly detection? Imagine you’re an aircraft engine manufacturer As engines roll off your assembly line you’re doing QA Read more…
Motivation 1: Data compression Start talking about a second type of unsupervised learning problem – dimensionality reduction Why should we look at dimensionality reduction? Compression Speeds up algorithms Reduces space used by data for them What is dimensionality reduction? So you’ve collected many features – maybe more than you need Can you “simply” Read more…