Goto

Collaborating Authors

jupyter notebook


Exploring the Most Popular Machine Learning and Deep Learning GitHub Repositories

#artificialintelligence

Currently, machine learning and deep learning are two subjects of broad interest in both academia and industry. Given their immense popularity, there are hundreds of thousands of GitHub repositories that exist, which contain the source code, documentation, and other useful information on a vast number projects related to either topic. In this article, I explain the process for how I collected, cleaned, and visualized the data on a selection of the most popular machine learning and deep learning GitHub repositories. I also discuss the trends, patterns, and key findings that are related to each of the visualizations that I created. You can find all of my source code that supports this article in my own GitHub repository here.


Python for Machine Learning: A Tutorial

#artificialintelligence

Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it's important that you have a basic understanding of how it works with machine learning. In this introductory tutorial, you'll learn the basics of Python for machine learning, including different model types and the steps to take to ensure you obtain quality data, using a sample machine learning problem. In addition, you'll get to know some of the most popular libraries and tools for machine learning. Machine learning (ML) is a form of artificial intelligence (AI) that teaches computers to make predictions and recommendations and solve problems based on data. Its problem-solving capabilities make it a useful tool in industries such as financial services, healthcare, marketing and sales, and education among others. There are three main types of machine learning: supervised, unsupervised, and reinforcement. In supervised learning, the computer is given a set of training data that includes both the input data (what we want to predict) and the output data (the prediction).


Intro to MLOps using Amazon SageMaker

#artificialintelligence

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider...


How to test ML models in the real world

#artificialintelligence

How often do you test ML models in a Jupyter notebook, get good results, but still cannot convince your boss that the model should be used right away? Or maybe you manage to convince her and put the model in production, but you do not see any impact on business metrics? Luckily for you, there are better ways to test ML models in the real world and to convince everyone (including you) that they add value to the business. In this article you will learn what these evaluation methods are, how to implement them, and when should you use each. We, data scientists and ML engineers, develop and test ML models in our local development environment, for example, a Jupyter notebook.


How to develop machine learning skills in all of your company's employees

#artificialintelligence

Everyone loves Artificial Intelligence (AI) and data science (DS), and it's probably not going to change for the next decade or so. Still, most people only have a general idea of what data science is and what machine learning algorithms or AI can do. This is quite normal and a common phenomenon for all fields of expertise. Think about it: do you really know what DevOps, Support or NOC (Network Operation Center) actually do? Sure, as tech professionals we can probably explain it better than people who aren't part of the industry, but in most cases it's pretty hard to really understand what other people are doing if you've never done it yourself.


My First Experience Deploying an ML Model to Production

#artificialintelligence

I have been working on Machine Learning since my third year in college. But during this time, the process always involved taking the dataset from Kaggle or some other open-source website. Also, these models/algorithms were either there on some Jupyter Notebook or Python script and were not deployed to some production website, it was always localhost. While interning at HackerRank and also after starting as a Software Engineer here as a part of the HackerRank Labs team working on a new product, I got a chance to deploy three different ML Models to production, working end-to-end on them. In this blog, I will be sharing my learnings and experience from one of the deployed models.


Luminide AI Model Development

#artificialintelligence

Luminide makes it easier to build better AI models. Luminide contains all of the hardware and software you need. And automates all of the tedious, repetitive tasks. Includes Jupyter notebooks, TensorBoard, and Experiment Tracking. Luminide contains optimizations like Hyperparameter Tuning and Early Ranking to build better AI models.


3 + 1 ways of running R on Amazon SageMaker

#artificialintelligence

The R programming language is one of the most commonly used languages in the scientific space, being one of the most commonly used languages for machine learning (probably second following python) and arguably the most popular language amongst mathematicians and statisticians. It is easy to get started with, free to use, with support for many scientific and visualisation libraries. While R can help you analyse your data, the more data you have the more compute power you require and the more impactful your analysis is, the more repeatability and reproducibility is required. Analysts and Data Scientists need to find ways to fulfil such requirements. In this post we briefly describe the main ways of running your R workloads on the cloud, making use of Amazon SageMaker, the end-to-end Machine Learning cloud offering of AWS.


Quick Data Science Tips and Tricks to Learn SAS - KDnuggets

#artificialintelligence

We all know there's the demand for data science skills continues to grow at exponential rates. And seemingly our available time to learn seems to decrease. But sometimes all you need is to learn a quick tip or trick to solve the task at hand, or only have time to dedicate short increments of time to learn a new skill. Regardless of the scenario, you can tune in to the SAS Users YouTube channel where several SAS Tutorials are posted each month. You'll hear from a variety of experts on different topics for various skill levels.


Using Kaggle in Machine Learning Projects

#artificialintelligence

You've probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours. You can also look at other people's public notebooks as well! Other than the website, Kaggle also has a command-line interface (CLI) which you can use within the command line to access and download datasets.