Showing posts with the label Kaggle

Walk-through : Getting started with Keras, using Kaggle

Walkthrough: Starting with Kegel, using Kegel This is a short post - the real substance is in the screencast below, where I go through the code! If you’re just starting out in the world of machine learning (and let it be real here, who isn’t?), Tooling seems to be getting better and better. Keras has been a major tool for some time and is now integrated into the TensorFlow. Good together, right? And it just so happens that it has never been easier to get started with Keras. But wait, what exactly is Keras, and how can you use it to start building your own machine learning models? Today, I want to show you how to get started with bananas in the fastest possible way. Canara is not only built on TensorFlow via TensorFlow.Caras, you don't have to install or configure anything if you use tools like the Kegel kernel. Introduction to Kaggle Kernel Find out what Keggel kernels are and how to get started. While not there ... Playing around with Ker

Going with Scikit-Learn on Kaggle

Go to Kaggle with a bicycle-learner Psych-learning has long been a popular library for beginners with machine learning. However, not everyone has the opportunity to use it yet. I will show you how to go cycling-learning along the short route, all you need is a web browser! Brief history text Let’s start for reference with a little dash of history. Psychit-Learn was originally called Psychits. Laren and David Cornepia began life as the Google Summer of Code project. The name 'Psychit' comes from the Saipan Toolkit. Since then, psychic-learners have consistently embraced it and gained popularity today: a well-documented, well-documented Python machine learning library. If you take a look at, you will notice that the version number is quite low, 0.0 this as of this post. Don't be afraid of it; The library has been around for a long time and is well maintained and quite reliable. What does a psychic learner do? What's really neat about it is that it's a

Data science project using Kaggle Datasets and Kernels

Cooking a data science project using Kaggle dataset and kernel We are working together to use fresh materials (data), to prepare them using different tools, and to work together with a delicious result - a published dataset and some quiet analysis that we are sharing with the world. Working with dataset and kernel We will pull public data from the city of Los Angeles open data portal, including environmental health violations from restaurants in Los Angeles. So we will create new datasets using the data, and work together on the kernel before releasing it into the world. In this blog you will learn: How to create a new, private, Kaggal dataset from raw data How to share your dataset before making it public to those involved in your collaboration Adding helpers to private kernels How to use helpers in Koggle kernels Data is most powerful when it is reproducible code and shared with experts and the community at large. By placing data and code on a shared, consistent platform, you get the

Introduction to Kaggle Kernels

Introduction to Kaggle Kernel Kaggle is a platform for data science and sharing. You may have heard of some of their competitions, which often have cash prizes. It is also a great place to practice data science and learn from the community. What are Kaggle kernels? Kaggle kernels are basically Jupiter notebooks in the browser that run right before your eyes, all for free. Tell me again that in this case, you missed it, because it's so amazing: Kaggle is a free platform for running Jupiter notebooks in the kernel browser! This means you can avoid the hassle of setting up a local environment and have a Jupyter notebook environment inside your browser, wherever you have an internet connection anywhere in the world. Not only that, but the processing power for notebooks also comes from the server in the cloud, not as your local machine, so you can do a lot of data burning and machine learning on the laptop battery!