- Feature Engineering of DateTime Variables for Data Science, Machine Learning - Apr 29, 2021.
Learn how to make more meaningful features from DateTime type variables to be used by Machine Learning Models.
- Multiple Time Series Forecasting with PyCaret - Apr 27, 2021.
A step-by-step tutorial to forecast multiple time series with PyCaret.
- Production-Ready Machine Learning NLP API with FastAPI and spaCy - Apr 21, 2021.
Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.
- Time Series Forecasting with PyCaret Regression Module - Apr 21, 2021.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.
- Top 10 Data Science Courses to Take in 2021 - Apr 20, 2021.
Whether you are getting started with Data Science / Machine Learning or are an experienced professional looking to learn something new, check out these top 10 data science courses for 2021.
- Data Analysis Using Tableau - Apr 20, 2021.
Read this overview of using Tableau for sale data analysis, and see how visualization can help tell the business story.
- Essential Math for Data Science: Linear Transformation with Matrices - Apr 16, 2021.
You’ll start seeing matrices, not only as operations on numbers, but also as a way to transform vector spaces. This conception will give you the foundations needed to understand more complex linear algebra concepts like matrix decomposition.
- The Most In-Demand Skills for Data Scientists in 2021 - Apr 15, 2021.
If you are preparing to make a career as a Data Scientist or are looking for opportunities to skill-up in your current role, this analysis of in-demand skills for 2021, based on over 15,000 Data Scientist job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.
- Is Your Model Overtained? - Apr 14, 2021.
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
- Automated Anomaly Detection Using PyCaret - Apr 13, 2021.
Learn to automate anomaly detection using the open source machine learning library PyCaret.
- How to Apply Transformers to Any Length of Text - Apr 12, 2021.
Read on to find how to restore the power of NLP for long sequences.
- E-commerce Data Analysis for Sales Strategy Using Python - Apr 7, 2021.
Check out this informative and concise case study applying data analysis using Python to a well-defined e-commerce scenario.
- KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable - Apr 7, 2021.
Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1
- Automated Text Classification with EvalML - Apr 6, 2021.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
- The Best Machine Learning Frameworks & Extensions for TensorFlow - Apr 5, 2021.
Check out this curated list of useful frameworks and extensions for TensorFlow.
- Shapash: Making Machine Learning Models Understandable - Apr 2, 2021.
Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
- Easy AutoML in Python - Apr 1, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- Software Engineering Best Practices for Data Scientists - Mar 30, 2021.
This is a crash course on how to bridge the gap between data science and software engineering.
- Data Science Curriculum for Professionals - Mar 25, 2021.
If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
- Extraction of Objects In Images and Videos Using 5 Lines of Code - Mar 25, 2021.
PixelLib is a library created for easy integration of image and video segmentation in real life applications. Learn to use PixelLib to extract objects In images and videos with minimal code.
- Top 10 Python Libraries Data Scientists should know in 2021 - Mar 24, 2021.
So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
- Rejection Sampling with Python - Mar 24, 2021.
Read this article on rejection sampling with examples using the Normal and Cauchy Distributions.
- The Best Machine Learning Frameworks & Extensions for Scikit-learn - Mar 22, 2021.
Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.
- How to build a DAG Factory on Airflow - Mar 19, 2021.
A guide to building efficient DAGs with half of the code.
- A Simple Way to Time Code in Python - Mar 18, 2021.
Read on to find out how to use a decorator to time your functions.
- How to Begin Your NLP Journey - Mar 17, 2021.
In this blog post, learn how to process text using Python.
- KDnuggets™ News 21:n11, Mar 17: Is Data Scientist still a satisfying job? How To Overcome The Fear of Math and Learn Math For Data Science - Mar 17, 2021.
Must Know for Data Scientists and Data Analysts: Causal Design Patterns; Know your data much faster with the new Sweetviz Python library; The Inferential Statistics Data Scientists Should Know; Natural Language Processing Pipelines, Explained
- Natural Language Processing Pipelines, Explained - Mar 16, 2021.
This article presents a beginner's view of NLP, as well as an explanation of how a typical NLP pipeline might look.
- Kedro-Airflow: Orchestrating Kedro Pipelines with Airflow - Mar 12, 2021.
The Kedro team and Astronomer have released Kedro-Airflow 0.4.0 to help you develop modular, maintainable & reproducible code with orchestration superpowers!
- Know your data much faster with the new Sweetviz Python library - Mar 12, 2021.
One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and how to use it here.
- How to Speed Up Pandas with Modin - Mar 10, 2021.
The Modin library has the ability to scale your pandas workflows by changing one line of code and integration with the Python ecosystem and Ray clusters. This tutorial goes over how to get started with Modin and how it can speed up your pandas workflows.
- 4 Machine Learning Concepts I Wish I Knew When I Built My First Model - Mar 9, 2021.
Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.
- Beautiful decision tree visualizations with dtreeviz - Mar 8, 2021.
Improve the old way of plotting the decision trees and never go back!
- 11 Essential Code Blocks for Complete EDA (Exploratory Data Analysis) - Mar 5, 2021.
This article is a practical guide to exploring any data science project and gain valuable insights.
- Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - Mar 5, 2021.
PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.
- 9 Skills You Need to Become a Data Engineer - Mar 4, 2021.
A data engineer is a fast-growing profession with amazing challenges and rewards. Which skills do you need to become a data engineer? In this post, we’ll take a look at both hard and soft skills.
- 15 common mistakes data scientists make in Python (and how to fix them) - Mar 3, 2021.
Writing Python code that works for your data science project and performs the task you expect is one thing. Ensuring your code is readable by others (including your future self), reproducible, and efficient are entirely different challenges that can be addressed by minimizing common bad practices in your development.
- Getting Started with Distributed Machine Learning with PyTorch and Ray - Mar 3, 2021.
Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.
- Speech to Text with Wav2Vec 2.0 - Mar 2, 2021.
Facebook recently introduced and open-sourced their new framework for self-supervised learning of representations from raw audio data called Wav2Vec 2.0. Learn more about it and how to use it here.
- Are You Still Using Pandas to Process Big Data in 2021? Here are two better options - Mar 1, 2021.
When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?
- Data Science Learning Roadmap for 2021 - Feb 26, 2021.
Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.
- Pandas Profiling: One-Line Magical Code for EDA - Feb 24, 2021.
EDA can be automated using a Python library called Pandas Profiling. Let’s explore Pandas profiling to do EDA in a very short time and with just a single line code.
- KDnuggets™ News 21:n08, Feb 24: Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer - Feb 24, 2021.
Powerful Exploratory Data Analysis in just two lines of code; Cartoon: Data Scientist vs Data Engineer; Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall; Feature Store as a Foundation for Machine Learning; Approaching (Almost) Any Machine Learning Problem
- Powerful Exploratory Data Analysis in just two lines of code - Feb 22, 2021.
EDA is a fundamental early process for any Data Science investigation. Typical approaches for visualization and exploration are powerful, but can be cumbersome for getting to the heart of your data. Now, you can get to know your data much faster with only a few lines of code... and it might even be fun!
- Multidimensional multi-sensor time-series data analysis framework - Feb 19, 2021.
This blog post provides an overview of the package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided.
- Approaching (Almost) Any Machine Learning Problem - Feb 18, 2021.
This freely-available book is a fantastic walkthrough of practical approaches to machine learning problems.
- Distributed and Scalable Machine Learning [Webinar] - Feb 17, 2021.
Mike McCarty and Gil Forsyth work at the Capital One Center for Machine Learning, where they are building internal PyData libraries that scale with Dask and RAPIDS. For this webinar, Feb 23 @ 2 pm PST, 5pm EST, they’ll join Hugo Bowne-Anderson and Matthew Rocklin to discuss their journey to scale data science and machine learning in Python.
- 10 resources for data science self-study - Feb 17, 2021.
Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.
- Easy, Open-Source AutoML in Python with EvalML - Feb 16, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
- How to Speed up Scikit-Learn Model Training - Feb 11, 2021.
Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?
- 7 Most Recommended Skills to Learn to be a Data Scientist - Feb 10, 2021.
The Data Scientist professional has emerged as a true interdisciplinary role that spans a variety of skills, theoretical and practical. For the core, day-to-day activities, many critical requirements that enable the delivery of real business value reach well outside the realm of machine learning, and should be mastered by those aspiring to the field.
- KDnuggets™ News 21:n06, Feb 10: The Best Data Science Project to Have in Your Portfolio; Deep learning doesn’t need to be a black box - Feb 10, 2021.
The Best Data Science Project to Have in Your Portfolio; Deep learning doesn’t need to be a black box; Build Your First Data Science Application; How to create stunning visualizations using python from scratch; How to Get Your First Job in Data Science without Any Work Experience
- How to Deploy a Flask API in Kubernetes and Connect it with Other Micro-services - Feb 9, 2021.
A hands-on tutorial on how to implement your micro-service architecture using the powerful container orchestration tool Kubernetes.
- Essential Math for Data Science: Introduction to Matrices and the Matrix Product - Feb 5, 2021.
As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.
- Build Your First Data Science Application - Feb 4, 2021.
Check out these seven Python libraries to make your first data science MVP application.
- How to create stunning visualizations using python from scratch - Feb 4, 2021.
Data science and data analytics can be beautiful things. Not only because of the insights and enhancements to decision-making they can provide, but because of the rich visualizations about the data that can be created. Following this step-by-step guide using the Matplotlib and Seaborn libraries will help you improve the presentation and effective communication of your work.
- Getting Started with 5 Essential Natural Language Processing Libraries - Feb 3, 2021.
This article is an overview of how to get started with 5 popular Python NLP libraries, from those for linguistic data visualization, to data preprocessing, to multi-task functionality, to state of the art language modeling, and beyond.
- Working With The Lambda Layer in Keras - Jan 28, 2021.
In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.
- Mastering TensorFlow Variables in 5 Easy Steps - Jan 20, 2021.
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.
- Loglet Analysis: Revisiting COVID-19 Projections - Jan 20, 2021.
We will show that the decomposition of growth into S-shaped logistic components also known as Loglet analysis, is more accurate as it takes into account the evolution of multiple covid waves.
- Comprehensive Guide to the Normal Distribution - Jan 18, 2021.
Drop in for some tips on how this fundamental statistics concept can improve your data science.
- Snowflake and Saturn Cloud Partner To Bring 100x Faster Data Science to Millions of Python Users - Jan 15, 2021.
Snowflake the cloud data platform, is partnering, integrating products, and pursuing a joint go-to-market with Saturn Cloud to help data science teams get 100x faster results. Read more about developments and how to get started here.
- Cleaner Data Analysis with Pandas Using Pipes - Jan 15, 2021.
Check out this practical guide on Pandas pipes.
- KDnuggets™ News 21:n02, Jan 13: Best Python IDEs and Code Editors; 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 13, 2021.
Best Python IDEs and Code Editors You Should Know; 10 Underappreciated Python Packages for Machine Learning Practitioners; Top 10 Computer Vision Papers 2020; CatalyzeX: A must-have browser extension for machine learning engineers and researchers
- Creating Good Meaningful Plots: Some Principles - Jan 12, 2021.
Hera are some thought starters to help you create meaningful plots.
- 5 Tools for Effortless Data Science - Jan 11, 2021.
The sixth tool is coffee.
- Best Python IDEs and Code Editors You Should Know - Jan 8, 2021.
Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.
- 10 Underappreciated Python Packages for Machine Learning Practitioners - Jan 7, 2021.
Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.
- KDnuggets™ News 21:n01, Jan 6: All machine learning algorithms you should know in 2021; Monte Carlo integration in Python; MuZero – the most important ML system ever created? - Jan 6, 2021.
The first issue in 2021 brings you a great blog about Monte Carlo Integration - in Python; An overview of main Machine Learning algorithms you need to know in 2021; SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations - how to; MuZero - may be the most important Machine Learning system ever created; and much more!
- 15 Free Data Science, Machine Learning & Statistics eBooks for 2021 - Dec 31, 2020.
We present a curated list of 15 free eBooks compiled in a single location to close out the year.
- Generating Beautiful Neural Network Visualizations - Dec 30, 2020.
If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out.
- Monte Carlo integration in Python - Dec 24, 2020.
A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?
- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance - Dec 21, 2020.
A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
- Fast and Intuitive Statistical Modeling with Pomegranate - Dec 21, 2020.
Pomegranate is a delicious fruit. It can also be a super useful Python library for statistical analysis. We will show how in this article.
- How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
- KDnuggets™ News 20:n47, Dec 16: A Rising Library Beating Pandas in Performance; R or Python? Why Not Both? - Dec 16, 2020.
Also: 10 Python Skills They Don't Teach in Bootcamp; Data Science Volunteering: Ways to Help; A Journey from Software to Machine Learning Engineer; Data Science and Machine Learning: The Free eBook
- Data Science and Machine Learning: The Free eBook - Dec 15, 2020.
Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.
- How to Create Custom Real-time Plots in Deep Learning - Dec 14, 2020.
How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.
- Matrix Decomposition Decoded - Dec 11, 2020.
This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.
- A Rising Library Beating Pandas in Performance - Dec 11, 2020.
This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.
- 10 Python Skills They Don’t Teach in Bootcamp - Dec 11, 2020.
Ascend to new heights in Data Science and Machine Learning with this thrilling list of coding tips.
- Implementing the AdaBoost Algorithm From Scratch - Dec 10, 2020.
AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.
- Data Compression via Dimensionality Reduction: 3 Main Methods - Dec 10, 2020.
Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.
- R or Python? Why Not Both? - Dec 9, 2020.
Do you use both R and Python, either in different projects or in the same? Check out prython, an IDE designed to handle your needs.
- Merging Pandas DataFrames in Python - Dec 8, 2020.
A quick how-to guide for merging Pandas DataFrames in Python.
- Change the Background of Any Video with 5 Lines of Code - Dec 7, 2020.
Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.
- Pruning Machine Learning Models in TensorFlow - Dec 4, 2020.
Read this overview to learn how to make your models smaller via pruning.
- 10 Python Skills for Beginners - Dec 3, 2020.
Python is the fastest growing, most-beloved programming language. Get started with these Data Science tips.
- KDnuggets™ News 20:n45, Dec 2: TabPy: Combining Python and Tableau; Learn Deep Learning with this Free Course from Yann LeCun - Dec 2, 2020.
Combine Python and Tableau with TabPy; Learn Deep Learning with this Free Course from Yann LeCun; Find 15 Exciting AI Project Ideas for Beginners; Read about the Rise of the Machine Learning Engineer; See How to Incorporate Tabular Data with HuggingFace Transformers
- Object-Oriented Programming Explained Simply for Data Scientists - Dec 1, 2020.
Read this simple but effective guide to start using Classes in Python 3.
- Deploying Trained Models to Production with TensorFlow Serving - Nov 30, 2020.
TensorFlow provides a way to move a trained model to a production environment for deployment with minimal effort. In this article, we’ll use a pre-trained model, save it, and serve it using TensorFlow Serving.
- Data Science History and Overview - Nov 30, 2020.
In this era of big data that is only getting bigger, a huge amount of information from different fields is gathered and stored. Its analysis and extraction of value have become one of the most attractive tasks for companies and society in general, which is harnessed by the new professional role of the Data Scientist.
- 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 38 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.