Introducing an robust machine learning template. Providing a well-structured generic code base which can be easily tweaked according to your use case. Using recent packages (DVC and MLFlow) to ensure reproducibility of model results and effective model performance tracking.
TL/DR: I’ve developed a package on Github, [ml-template_](https://github.com/eddiepease/ml-template), which speeds up the development of local machine learning models by:_
Although it seems like yesterday, I now started out on my machine learning journey 4+ years ago. In that time, I have been lucky enough to tackle a number of cool problems. From writing an algorithm to detect someone’s gender based on a picture of their shoes, to using natural language processing to predict your next job given your CV to predicting the outcome of clinical trials, I have worked on a wide variety of problems.
These problems are clearly quite different and have their own unique challenges. What interests me for this post, though, is not how these problems are different but what they have in common. Every time I have started coding-up a machine learning problem, I have had to write the same objects and functions to split the training and test sets, train the algorithm, perform cross-validation, save the trained model and so on. This has applied regardless of whether it is a natural language problem, a machine learning vision or any other type of problem.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Data Science for Social Good: Best Sources for Free Open Data. In this article, I am going through some of the most well-known and important portals that can be used in this regard.
Data Science Pull Requests — A Method for Data Science Review & Merging. A step forward for MLOps and unlocking Open Source Data Science
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.