- Essential Math for Data Science: Integrals And Area Under The Curve - Nov 25, 2020.
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.
- How to Incorporate Tabular Data with HuggingFace Transformers - Nov 25, 2020.
In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.
- Simple Python Package for Comparing, Plotting & Evaluating Regression Models - Nov 25, 2020.
This package is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
- TabPy: Combining Python and Tableau - Nov 24, 2020.
This article demonstrates how to get started using Python in Tableau.
- Computer Vision at Scale With Dask And PyTorch - Nov 23, 2020.
A tutorial on conducting image classification inference using the Resnet50 deep learning model at scale with using GPU clusters on Saturn Cloud. The results were: 40x faster computer vision that made a 3+ hour PyTorch model run in just 5 minutes.
- Top 6 Data Science Programs for Beginners - Nov 20, 2020.
Udacity has the best industry-leading programs in data science. Here are the top six data science courses for beginners to help you get started.
- KDnuggets™ News 20:n44, Nov 18: How to Acquire the Most Wanted Data Science Skills; Learn to build an end to end data science project - Nov 18, 2020.
How to get the most wanted Data Science skills; How to build and end to end Data Science project; How to get into Data Science without a degree; Top Python Libraries for Deep Learning, Natural Language Processing, and Computer Vision; Is Data Science for you? 14 self-examination questions to consider; and more
- Algorithms for Advanced Hyper-Parameter Optimization/Tuning - Nov 17, 2020.
In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.
- 5 Things You Are Doing Wrong in PyCaret - Nov 16, 2020.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. Find out 5 ways to improve your usage of the library.
- Top Python Libraries for Deep Learning, Natural Language Processing & Computer Vision - Nov 16, 2020.
This article compiles the 30 top Python libraries for deep learning, natural language processing & computer vision, as best determined by KDnuggets staff.
- From Y=X to Building a Complete Artificial Neural Network - Nov 13, 2020.
In this tutorial, we will start with the most simple artificial neural network (ANN) and move to something much more complex. We begin by building a machine learning model with no parameters—which is Y=X.
- tensorflow + dalex = :) , or how to explain a TensorFlow model - Nov 13, 2020.
Having a machine learning model that generates interesting predictions is one thing. Understanding why it makes these predictions is another. For a tensorflow predictive model, it can be straightforward and convenient develop an explainable AI by leveraging the dalex Python package.
- Learn to build an end to end data science project - Nov 11, 2020.
Appreciating the process you must work through for any Data Science project is valuable before you land your first job in this field. With a well-honed strategy, such as the one outlined in this example project, you will remain productive and consistently deliver valuable machine learning models.
- Mastering TensorFlow Tensors in 5 Easy Steps - Nov 11, 2020.
Discover how the building blocks of TensorFlow works at the lower level and learn how to make the most of Tensor objects.
- KDnuggets™ News 20:n43, Nov 11: The Best Data Science Certification You’ve Never Heard Of; Essential data science skills that no one talks about - Nov 11, 2020.
The Best Data Science Certification You've Never Heard Of; Essential data science skills that no one talks about; Pandas on Steroids: End to End Data Science in Python with Dask; How to Build a Football Dataset with Web Scraping; 2 Coding-free Ways to Extract Content From Websites to Boost Web Traffic
- Every Complex DataFrame Manipulation, Explained & Visualized Intuitively - Nov 10, 2020.
Most Data Scientists might hail the power of Pandas for data preparation, but many may not be capable of leveraging all that power. Manipulating data frames can quickly become a complex task, so eight of these techniques within Pandas are presented with an explanation, visualization, code, and tricks to remember how to do it.
- Change the Background of Any Image with 5 Lines of Code - Nov 9, 2020.
Blur, color, grayscale and change the background of any image with a picture using PixelLib.
- Pandas on Steroids: End to End Data Science in Python with Dask - Nov 6, 2020.
End to end parallelized data science from reading big data to data manipulation to visualisation to machine learning.
- How to Build a Football Dataset with Web Scraping - Nov 5, 2020.
- How to deploy PyTorch Lightning models to production - Nov 5, 2020.
A complete guide to serving PyTorch Lightning models at scale.
- KDnuggets™ News 20:n42, Nov 4: Top Python Libraries for Data Science, Data Visualization & Machine Learning; Mastering Time Series Analysis - Nov 4, 2020.
Top Python Libraries for Data Science, Data Visualization, Machine Learning; Mastering Time Series Analysis with Help From the Experts; Explaining the Explainable AI: A 2-Stage Approach; The Missing Teams For Data Scientists; and more.
- Building Deep Learning Projects with fastai — From Model Training to Deployment - Nov 4, 2020.
A getting started guide to develop computer vision application with fastai.
- Top Python Libraries for Data Science, Data Visualization & Machine Learning - Nov 2, 2020.
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
- Building Neural Networks with PyTorch in Google Colab - Oct 30, 2020.
Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that.
- Dealing with Imbalanced Data in Machine Learning - Oct 29, 2020.
This article presents tools & techniques for handling data when it's imbalanced.
- Stop Running Jupyter Notebooks From Your Command Line - Oct 28, 2020.
Instead, run your Jupyter Notebook as a stand alone web app.
- Which flavor of BERT should you use for your QA task? - Oct 22, 2020.
Check out this guide to choosing and benchmarking BERT models for question answering.
- 10 Underrated Python Skills - Oct 21, 2020.
Tips for feature analysis, hyperparameter tuning, data visualization and more.
- KDnuggets™ News 20:n40, Oct 21: fastcore: An Underrated Python Library; Goodhart’s Law for Data Science: what happens when a measure becomes a target? - Oct 21, 2020.
fastcore: An Underrated Python Library; Goodhart's Law for Data Science and what happens when a measure becomes a target?; Text Mining with R: The Free eBook; Free From MIT: Intro to Computational Thinking and Data Science; How to ace the data science coding challenge
- Deploying Streamlit Apps Using Streamlit Sharing - Oct 20, 2020.
Read this sneak peek into Streamlit’s new deployment platform.
- Data Science in the Cloud with Dask - Oct 20, 2020.
Scaling large data analyses for data science and machine learning is growing in importance. Dask and Coiled are making it easy and fast for folks to do just that. Read on to find out how.
- Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
This article covers using scikit-learn to obtain the optimal number of features for your machine learning project.
- Roadmap to Natural Language Processing (NLP) - Oct 19, 2020.
Check out this introduction to some of the most common techniques and models used in Natural Language Processing (NLP).
- Fast Gradient Boosting with CatBoost - Oct 16, 2020.
In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.
- fastcore: An Underrated Python Library - Oct 15, 2020.
A unique python library that extends the python programming language and provides utilities that enhance productivity.
- Free From MIT: Intro to Computational Thinking and Data Science - Oct 14, 2020.
This free course from MIT will help in your transition to thinking computationally, and ultimately solving complex data science problems.
- Getting Started with PyTorch - Oct 14, 2020.
A practical walkthrough on how to use PyTorch for data analysis and inference.
- Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases.
- Here are the Most Popular Python IDEs/Editors - Oct 6, 2020.
Jupyter Notebook continues to lead as the most popular Python IDE, but its share has declined since the last poll. The top 4 contenders have remained the same, but only one has significantly improved its share. We also examine the breakdown by employment and region.
- 10 Best Machine Learning Courses in 2020 - Oct 6, 2020.
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
- Your Guide to Linear Regression Models - Oct 5, 2020.
This article explains linear regression and how to program linear regression models in Python.
- Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code - Oct 5, 2020.
Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.
- Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science - Oct 1, 2020.
Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
- KDnuggets™ News 20:n37, Sep 30: Introduction to Time Series Analysis in Python; How To Improve Machine Learning Model Accuracy - Sep 30, 2020.
Learn how to work with time series in Python; Tips for improving Machine Learning model accuracy from 80% to over 90%; Geographical Plots with Python; Best methods for making Python programs blazingly fast; Read a complete guide to PyTorch; KDD Best Paper Awards and more.
- Looking Inside The Blackbox: How To Trick A Neural Network - Sep 28, 2020.
In this tutorial, I’ll show you how to use gradient ascent to figure out how to misclassify an input.
- Geographical Plots with Python - Sep 28, 2020.
When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.
- Making Python Programs Blazingly Fast - Sep 25, 2020.
Let’s look at the performance of our Python programs and see how to make them up to 30% faster!
- Create and Deploy your First Flask App using Python and Heroku - Sep 25, 2020.
Flask is a straightforward and lightweight web application framework for Python applications. This guide walks you through how to write an application using Flask with a deployment on Heroku.
- Introduction to Time Series Analysis in Python - Sep 24, 2020.
Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data.
- The Most Complete Guide to PyTorch for Data Scientists - Sep 24, 2020.
All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.
- KDnuggets™ News 20:n36, Sep 23: New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project - Sep 23, 2020.
New Poll: What Python IDE / Editor you used the most in 2020?; Automating Every Aspect of Your Python Project; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python; Online Certificates/Courses in AI, Data Science, Machine Learning; Can Neural Networks Show Imagination?
- New Poll: What Python IDE / Editor you used the most in 2020? - Sep 22, 2020.
The latest KDnuggets polls asks which Python IDE / Editor you have used the most in 2020. Participate now, and share your experiences with the community.
- Statistical and Visual Exploratory Data Analysis with One Line of Code - Sep 21, 2020.
If EDA is not executed correctly, it can cause us to start modeling with “unclean” data. See how to use Pandas Profiling to perform EDA with a single line of code.
- Automating Every Aspect of Your Python Project - Sep 18, 2020.
Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
- Implementing a Deep Learning Library from Scratch in Python - Sep 17, 2020.
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
- Autograd: The Best Machine Learning Library You’re Not Using? - Sep 16, 2020.
If there is a Python library that is emblematic of the simplicity, flexibility, and utility of differentiable programming it has to be Autograd.
- KDnuggets™ News 20:n35, Sep 16: Data Science Skills: Core, Emerging, and Most Wanted; Free From MIT: Intro to CS, Programming in Python - Sep 16, 2020.
Check the analysis of latest KDnuggets Poll: which data science skills are core, which are emerging, and what is the most wanted skill readers want to learn; Free From MIT: Intro to CS and Programming in Python; 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out; Statistics with Julia: The Free eBook; and more.
- Visualization Of COVID-19 New Cases Over Time In Python - Sep 15, 2020.
Inspired by another concise data visualization, the author of this article has crafted and shared the code for a heatmap which visualizes the COVID-19 pandemic in the United States over time.
- An Introduction to NLP and 5 Tips for Raising Your Game - Sep 11, 2020.
This article is a collection of things the author would like to have known when they started out in NLP. Perhaps it will be useful for you.
- Free From MIT: Intro to Computer Science and Programming in Python - Sep 9, 2020.
This free introductory computer science and programming course is available via MIT's Open Courseware platform. It's a great resource for mastering the fundamentals of one of data science's major requirements.
- Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills - Sep 8, 2020.
We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.
- 4 Tricks to Effectively Use JSON in Python - Sep 8, 2020.
Working with JSON in Python is a breeze, this will get you started right away.
- 10 Things You Didn’t Know About Scikit-Learn - Sep 3, 2020.
Check out these 10 things you didn’t know about Scikit-Learn... until now.
- Computer Vision Recipes: Best Practices and Examples - Sep 2, 2020.
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
- Which methods should be used for solving linear regression? - Sep 2, 2020.
As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.
- PyCaret 2.1 is here: What’s new? - Sep 1, 2020.
PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive. Read about what's new in PyCaret 2.1.
- Explainable and Reproducible Machine Learning Model Development with DALEX and Neptune - Aug 27, 2020.
With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have.
- Working with Spark, Python or SQL on Azure Databricks - Aug 27, 2020.
Here we look at some ways to interchangeably work with Python, PySpark and SQL using Azure Databricks, an Apache Spark-based big data analytics service designed for data science and data engineering offered by Microsoft.
- Data Science Tools Illustrated Study Guides - Aug 25, 2020.
These data science tools illustrated guides are broken up into four distinct categories: data retrieval, data manipulation, data visualization, and engineering tips. Both online and PDF versions of these guides are available.
- Rapid Python Model Deployment with FICO Xpress Insight - Aug 20, 2020.
The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.
- Build Your Own AutoML Using PyCaret 2.0 - Aug 20, 2020.
In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.
- The List of Top 10 Lists in Data Science - Aug 14, 2020.
The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.
- Bring your Pandas Dataframes to life with D-Tale - Aug 13, 2020.
Bring your Pandas dataframes to life with D-Tale. D-Tale is an open-source solution for which you can visualize, analyze and learn how to code Pandas data structures. In this tutorial you'll learn how to open the grid, build columns, create charts and view code exports.
- 5 Different Ways to Load Data in Python - Aug 13, 2020.
Data is the bread and butter of a Data Scientist, so knowing many approaches to loading data for analysis is crucial. Here, five Python techniques to bring in your data are reviewed with code examples for you to follow.
- GitHub is the Best AutoML You Will Ever Need - Aug 12, 2020.
This article uses PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions.
- Will Reinforcement Learning Pave the Way for Accessible True Artificial Intelligence? - Aug 11, 2020.
Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from its foundations, and how RL can support decision making in complex environments. Read more on the topic from the book's author Sebastian Raschka.
- Setting Up Your Data Science & Machine Learning Capability in Python - Aug 4, 2020.
With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.
- Announcing PyCaret 2.0 - Aug 3, 2020.
PyCaret 2.0 has been released! Find out about all of the updates and see examples of how to use them right here.
- The Machine Learning Field Guide - Aug 3, 2020.
This straightforward guide offers a structured overview of all machine learning prerequisites needed to start working on your project, including the complete data pipeline from importing and cleaning data to modelling and production.
- Fuzzy Joins in Python with d6tjoin - Jul 31, 2020.
Combining different data sources is a time suck! d6tjoin is a python library that lets you join pandas dataframes quickly and efficiently.
- Scaling Computer Vision Models with Dataflow - Jul 31, 2020.
Scaling Machine Learning models is hard and expensive. We will shortly introduce the Google Cloud service Dataflow, and how it can be used to run predictions on millions of images in a serverless way.
- A Complete Guide To Survival Analysis In Python, part 3 - Jul 30, 2020.
Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
- KDnuggets™ News 20:n29, Jul 29: Easy Guide To Data Preprocessing In Python; Building a better Spark UI; Computational Algebra for Coders: The Free Course - Jul 29, 2020.
An easy guide to data pre-processing in Python; Monitoring Apache Spark with a better Spark UI; Computational Linear Algebra for Coders: the free course; Labelling data with Snorkel; Bayesian Statistics.
- Building a Content-Based Book Recommendation Engine - Jul 28, 2020.
In this blog, we will see how we can build a simple content-based recommender system using Goodreads data.
- Labelling Data Using Snorkel - Jul 24, 2020.
In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.
- Easy Guide To Data Preprocessing In Python - Jul 24, 2020.
Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.
- Powerful CSV processing with kdb+ - Jul 23, 2020.
This article provides a glimpse into the available tools to work with CSV files and describes how kdb+ and its query language q raise CSV processing to a new level of performance and simplicity.
- Apache Spark Cluster on Docker - Jul 22, 2020.
Build your own Apache Spark cluster in standalone mode on Docker with a JupyterLab interface.
- KDnuggets™ News 20:n28, Jul 22: Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning - Jul 22, 2020.
Data Science MOOCs are too Superficial; The Bitter Lesson of Machine Learning; Building a REST API with Tensorflow Serving (Part 1); 3 Advanced Python Features You Should Know; Understanding How Neural Networks Think;
- Building a REST API with Tensorflow Serving (Part 2) - Jul 21, 2020.
This post is the second part of the tutorial of Tensorflow Serving in order to productionize Tensorflow objects and build a REST API to make calls to them.
- Recurrent Neural Networks (RNN): Deep Learning for Sequential Data - Jul 20, 2020.
Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.
- How to Handle Dimensions in NumPy - Jul 20, 2020.
Learn how to deal with Numpy matrix dimensionality using np.reshape, np.newaxis and np.expand_dims, illustrated with Python code.
- 3 Advanced Python Features You Should Know - Jul 16, 2020.
As a Data Scientist, you are already spending most of your time getting your data ready for prime time. Follow these real-world scenarios to learn how to leverage the advanced techniques in Python of list comprehension, Lambda expressions, and the Map function to get the job done faster.
- Building a REST API with Tensorflow Serving (Part 1) - Jul 15, 2020.
Part one of a tutorial to teach you how to build a REST API around functions or saved models created in Tensorflow. With Tensorflow Serving and Docker, defining endpoint URLs and sending HTTP requests is simple.
- KDnuggets™ News 20:n27, Jul 15: Great explanation of Calculus, the Key to Deep Learning; 8 data-driven reasons to learn Python - Jul 15, 2020.
We bring you free MIT courses on Calculus, which is the key to understanding Deep Learning - check this amazing explanation of an integral and dx; 8 data-driven reasons to learn Python; How to get and analyze Financial data with Python; Free ebook: The Foundations of Data Science and more.
- A Complete Guide To Survival Analysis In Python, part 2 - Jul 14, 2020.
Continuing with the second of this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter theory as well as the Nelson-Aalen fitter theory, both with examples and shared code.
- PyTorch LSTM: Text Generation Tutorial - Jul 13, 2020.
Key element of LSTM is the ability to work with sequences and its gating mechanism.
- Why Learn Python? Here Are 8 Data-Driven Reasons - Jul 10, 2020.
Through this blog, I will list out the major reasons why you should learn Python and the 8 major data-driven reasons for learning it.
- 5 Things You Don’t Know About PyCaret - Jul 9, 2020.
In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few words only.
- Learn Python, ML, Deep Learning, Data Visualization and more in Italy with BIG DIVE - Jul 9, 2020.
Do you want to learn or upgrade your data data proficiency and push your career forward? This year, under the umbrella of BIG DIVE, TOP-IX presents four full-time 1-week courses from beginner to advanced levels. Read more and register now.
- Pull and Analyze Financial Data Using a Simple Python Package - Jul 9, 2020.
We demonstrate a simple Python script/package to help you pull financial data (all the important metrics and ratios that you can think of) and plot them.
- Spam Filter in Python: Naive Bayes from Scratch - Jul 8, 2020.
In this blog post, learn how to build a spam filter using Python and the multinomial Naive Bayes algorithm, with a goal of classifying messages with a greater than 80% accuracy.
- KDnuggets™ News 20:n26, Jul 8: Speed up Your Numpy and Pandas; A Layman’s Guide to Data Science; Getting Started with TensorFlow 2 - Jul 8, 2020.
Speed up your Numpy and Pandas with NumExpr Package; A Layman's Guide to Data Science. Part 3: Data Science Workflow; Getting Started with TensorFlow 2; Feature Engineering in SQL and Python: A Hybrid Approach; Deploy Machine Learning Pipeline on AWS Fargate
- A Complete Guide To Survival Analysis In Python, part 1 - Jul 7, 2020.
This three-part series covers a review with step-by-step explanations and code for how to perform statistical survival analysis used to investigate the time some event takes to occur, such as patient survival during the COVID-19 pandemic, the time to failure of engineering products, or even the time to closing a sale after an initial customer contact.
- Exploratory Data Analysis on Steroids - Jul 6, 2020.
This is a central aspect of Data Science, which sometimes gets overlooked. The first step of anything you do should be to know your data: understand it, get familiar with it. This concept gets even more important as you increase your data volume: imagine trying to parse through thousands or millions of registers and make sense out of them.
- Feature Engineering in SQL and Python: A Hybrid Approach - Jul 2, 2020.
Set up your workstation, reduce workplace clutter, maintain a clean namespace, and effortlessly keep your dataset up-to-date.
- Getting Started with TensorFlow 2 - Jul 2, 2020.
Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.
- PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release - Jul 2, 2020.
PyTorch Lightning, a very light-weight structure for PyTorch, recently released version 0.8.1, a major milestone. With incredible user adoption and growth, they are continuing to build tools to easily do AI research.
- Speed up your Numpy and Pandas with NumExpr Package - Jul 1, 2020.
We show how to significantly speed up your mathematical calculations in Numpy and Pandas using a small library.
- Data Cleaning: The secret ingredient to the success of any Data Science Project - Jul 1, 2020.
With an uncleaned dataset, no matter what type of algorithm you try, you will never get accurate results. That is why data scientists spend a considerable amount of time on data cleaning.
- Data Science Tools Popularity, animated - Jun 25, 2020.
Watch the evolution of the top 10 most popular data science tools based on KDnuggets software polls from 2000 to 2019.
- Machine Learning in Dask - Jun 22, 2020.
In this piece, we’ll see how we can use Dask to work with large datasets on our local machines.
- 4 Free Math Courses to do and Level up your Data Science Skills - Jun 22, 2020.
Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.
- How to Deal with Missing Values in Your Dataset - Jun 22, 2020.
In this article, we are going to talk about how to identify and treat the missing values in the data step by step.
- The Most Important Fundamentals of PyTorch you Should Know - Jun 18, 2020.
PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.
- LightGBM: A Highly-Efficient Gradient Boosting Decision Tree - Jun 18, 2020.
LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.
- KDnuggets™ News 20:n24, Jun 17: Easy Speech-to-Text with Python; Data Distributions Overview; Java for Data Scientists - Jun 17, 2020.
Also: Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container; Five Cognitive Biases In Data Science (And how to avoid them); Understanding Machine Learning: The Free eBook; Simplified Mixed Feature Type Preprocessing in Scikit-Learn with Pipelines; A Complete guide to Google Colab for Deep Learning
- Simplified Mixed Feature Type Preprocessing in Scikit-Learn with Pipelines - Jun 16, 2020.
There is a quick and easy way to perform preprocessing on mixed feature type data in Scikit-Learn, which can be integrated into your machine learning pipelines.
- Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container - Jun 12, 2020.
In this tutorial, we will use a previously-built machine learning pipeline and Flask app to demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service.
- Easy Speech-to-Text with Python - Jun 10, 2020.
In this blog, I am demonstrating how to convert speech to text using Python. This can be done with the help of the “Speech Recognition” API and “PyAudio” library.
- Centroid Initialization Methods for k-means Clustering - Jun 10, 2020.
This article is the first in a series of articles looking at the different aspects of k-means clustering, beginning with a discussion on centroid initialization.
- Naïve Bayes Algorithm: Everything you need to know - Jun 8, 2020.
Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.
- Natural Language Processing with Python: The Free eBook - Jun 8, 2020.
This free eBook is an introduction to natural language processing, and to NLTK, one of the most prevalent Python NLP libraries.
- Deep Learning for Detecting Pneumonia from X-ray Images - Jun 5, 2020.
This article covers an end to end pipeline for pneumonia detection from X-ray images.
- Machine Learning Experiment Tracking - Jun 4, 2020.
Why is experiment tracking so important for doing real world machine learning?
- Introduction to Convolutional Neural Networks - Jun 3, 2020.
The article focuses on explaining key components in CNN and its implementation using Keras python library.
- Introduction to Pandas for Data Science - Jun 1, 2020.
The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.
- Model Evaluation Metrics in Machine Learning - May 28, 2020.
A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
- Taming Complexity in MLOps - May 28, 2020.
A greatly expanded v2.0 of the open-source Orbyter toolkit helps data science teams continue to streamline machine learning delivery pipelines, with an emphasis on seamless deployment to production.
- KDnuggets™ News 20:n21, May 27: The Best NLP with Deep Learning Course is Free; Your First Machine Learning Web App - May 27, 2020.
Also: Python For Everybody: The Free eBook; Complex logic at breakneck speed: Try Julia for data science; An easy guide to choose the right Machine Learning algorithm; Dataset Splitting Best Practices in Python; Appropriately Handling Missing Values for Statistical Modelling and Prediction