featuretools

To get started, I'd pick the most important business problem you have and then solve it using the simplest machine learning approach. You mentioned using Pytorch. Instead, I recommend a classical machine learning using a library like scikit-learn (https://scikit-learn.org/). Use a random forest classifer and you'll get pretty good results out of the box. If your data is in a postgres database across multiple tables, you will likely have to perform feature engineering in order to get it machine learning ready. For that, I recommend a library for automated feature engineering called Featuretools (http://github.com/featuretools/featuretools/). Here's a good article to get started with it (https://towardsdatascience.com/automated-feature-engineering...) Finally, you will need to define a prediction problem and extract labeled training example

I definitely see the need for packages like this. So much of a data scientist's time is spent on feature engineering, but there are relatively few tools out there that are trying to improve that step in the process compared to tools for the modeling step. I see this tool as a something that can help with the deployment piece of the feature engineering. As things stand, it's "easy" to package and deploy modeling code, but much harder to package up your feature engineering workflow. In part because there is no agreed upon standard for developing feature engineering pipelines. I'd be curious how this could be combined with a library like Featuretools (http://github.com/featuretools/featuretools/) which helps automate the discovery of features, but currently has less functionality related to deployment. (full disclosure: I work on Featuretoo

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site or Remote | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer API to automate time-intensive and error-prone parts of the data science process such as feature engineering. Our customers love our products because they make machine learning easier to use. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. If you're interested in getting hands on with our product, check out our open source library, Featuretools https://github.com/featuretools/featuretools/. Apply at https://www.featurelabs.com/ca

The challenge with machine learning today isn't that it doesn't work -- many organizations have successfully applied it to their problems -- but rather many people struggle to use it. There is massive potential if we can just make it easier to use the machine learning techniques that have already been proven to work. Phds are useful if you're trying to use the state of the art, but that's not what the masses need to benefit from machine learning. The scikit-learn library is a great example of this. It provides a clean fit()/predict() API that developers can leverage in their applications, which little understanding of how the implementations of each algorithms works. Another area ripe to be made easier for new practitioners of machine learning is feature engineering. Without proper feature engineering it is difficult to create accurate mo

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site or Remote | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer API to automate time-intensive and error-prone parts of the data science process such as feature engineering. Our customers love our products because they make machine learning easier to use. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. If you're interested in getting hands on with our product, check out our open source library, Featuretools https://github.com/featuretools/featuretools/. Apply at https://www.featurelabs.com/ca

Unlike most guides I've seen about ML, this one does a good job of focusing on developing and deploying a simple model first, then iterating. There are also lot of practical tips here, especially around feature engineering. > the second phase of machine learning involves pulling in as many features as possible and combining them in intuitive ways. During this phase, all of the metrics should still be rising As Google points out, after you build an initial model, the next step to increase accuracy is to perform feature engineering. They explain that this can be done manually or automatically using something like deep learning. Another option that people here might consider is using a library like Featuretools (https://github.com/featuretools/featuretools) for "automated feature engineering". Note: I am one of the developers. Our goal is to

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer API to automate time-intensive and error-prone parts of the data science process such as feature engineering. Our customers love our products because they make machine learning easier to use. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. If you're interested in getting hands on with our product, check out our open source library, Featuretools https://github.com/featuretools/featuretools/. Apply at https://www.featurelabs.com/careers/

A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science. One thing I didn't see covered in depth here was feature engineering, which is the process of preparing your raw data for the machine learning algorithms. They cover it briefly in the chapter on "Practical Considerations", but anyone looking to apply ML in the real world should look into feature engineering more on their own. One resource I recommend (and I am biased), is a python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets h

This seems like a great resource for understanding how machine learning algorithms actually work. Anyone reading the book to help them build more accurate models, might also be interested in supplementing it with more research into the importance of feature engineering before training machine learning models. While feature engineering hasn't been a rigorously studied within the academic literature, this book does have a section on feature generation, which gives some practical tips once your data is in feature matrix form. For those interested in even more, I work on a python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it t

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer API to automate time-intensive and error-prone parts of the data science process such as feature engineering. Our customers love our products because they make machine learning easier to use. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. If you're interested in getting hands on with our product, check out our open source library, Featuretools https://github.com/featuretools/featuretools/. Apply at https://www.featurelabs.com/careers/

I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning expertise are starting to apply ML to the problems they understand that best. One of the biggest roadblocks to this happening more today is that people don't know how to perform feature engineering to prepare raw data for existing machine learning algorithms. If we could automate this step, it would be a lot easier for subject matter experts to use ML. For example, I work on an open source python library called featuretools (https://github.com/featuretools/featuretools/) that aims automated feature engineering for relational datasets. We've seen a lot of non-ml people use it make their first machine learning models. We also have demos for people interested in trying it themselves: h

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and proprietary algorithms for automated feature engineering. With our technology, users can discover the most predictive patterns in behavioral and transactional datasets to build ML and AI products. We’re growing our technical team with engineers interested in pushing the boundaries of data science and feature engineering automation. In this role, you have the opportunity to define and implement data systems while collaborating with our top notch engineering team. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prior

Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/.... If anyone is looking to get more indepth, I work on an open source python library for automated feature engineering called Featuretools https://github.com/featuretools/featuretools/. It can help when your data is more complex such as when it comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos.

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and proprietary algorithms for automated feature engineering. With our technology, users can discover the most predictive patterns in behavioral and transactional datasets to build ML and AI products. We’re growing our technical team with engineers interested in pushing the boundaries of data science and feature engineering automation. In this role, you have the opportunity to define and implement data systems while collaborating with our top notch engineering team. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prior

This appears to be an thorough overview of machine learning. Even though many bases are covered, I wish there was more on how to create or select features for machine learning in a systematic way. Feature extraction gets 1 paragraph, but feature engineering and selection aren't mentioned much in the 200+ pages! I don't have a Phd in machine learning, but I have spent many years using it as a tool to solve problems. While the details here can get you a long way, without understand feature engineering or feature selection, you will have a hard time building accurate models. For any engineers looking for more on feature engineering after reading this, I maintain an open source library for automated feature engineering called Featuretools (https://github.com/featuretools/featuretools). We also have demos on our website (https://www.featureto