Search results for "PCA"

228 documents found out of 7199 total.

• A Gentle Introduction to Principal Component Analysis (PCA) in Python

  The most popular method for feature reduction and data compression, gently explained via implementation with Scikit-learn in Python.

  https://www.kdnuggets.com/gentle-introduction-principal-component-analysis-pca-in-python

• Principal Component Analysis (PCA) with Scikit-Learn

  Learn how to perform principal component analysis (PCA) in Python using the scikit-learn library.

  https://www.kdnuggets.com/2023/05/principal-component-analysis-pca-scikitlearn.html

• Essential Math for Data Science: Eigenvectors and Application to PCA

  In this article, you’ll learn about the eigendecomposition of a matrix.

  https://www.kdnuggets.com/2022/06/essential-math-data-science-eigenvectors-application-pca.html

• How to get Python PCAP Certification: Roadmap, Resources, Tips For Success, Based On My Experience

  Follow this journey of personal experience -- with useful tips and learning resources -- to help you achieve the PCAP Certification, one of the most reputed Python Certifications, to validate your knowledge against International Standards.

  https://www.kdnuggets.com/2021/09/python-pcap-certification-roadmap-resources.html

• Dimensionality Reduction with Principal Component Analysis (PCA)

  This article focuses on design principles of the PCA algorithm for dimensionality reduction and its implementation in Python from scratch.

  https://www.kdnuggets.com/2020/05/dimensionality-reduction-principal-component-analysis.html

• A comparison between PCA and hierarchical clustering

  Graphical representations of high-dimensional data sets are the backbone of exploratory data analysis. We examine 2 of the most commonly used methods: heatmaps combined with hierarchical clustering and principal component analysis (PCA).

  https://www.kdnuggets.com/2016/02/qlucore-comparison-pca-hierarchical-clustering.html

• 7 Statistical Concepts Every Data Scientist Should Master (and Why)

  Understanding data starts with statistics. These 7 statistics concepts give you the foundation to analyze and interpret with confidence.

  https://www.kdnuggets.com/7-statistical-concepts-every-data-scientist-should-master-and-why

• We Tried 5 Missing Data Imputation Methods: The Simplest Method Won (Sort Of)

  We tested five imputation methods with proper cross-validation and statistical testing. Mean imputation won for prediction but destroyed feature relationships.

  https://www.kdnuggets.com/we-tried-5-missing-data-imputation-methods-the-simplest-method-won-sort-of

• Prompt Engineering for Outlier Detection

  Learn how to detect outliers by doing a real-life data project and improve the process with AI.

  https://www.kdnuggets.com/prompt-engineering-for-outlier-detection

• The 5 FREE Must-Read Books for Every Machine Learning Engineer

  Learn the theory, math, and engineering behind machine learning with these highly recommended free books.

  https://www.kdnuggets.com/the-5-free-must-read-books-for-every-machine-learning-engineer

• The 5 FREE Must-Read Books for Every Data Scientist

  Want to level up your data skills? Check out these 5 free books that explain data science clearly and practically.

  https://www.kdnuggets.com/the-5-free-must-read-books-for-every-data-scientist

• 7 Steps to Build a Simple RAG System from Scratch

  This step-by-step tutorial walks you through building your own RAG system.

  https://www.kdnuggets.com/7-steps-to-build-a-simple-rag-system-from-scratch

• The 5 FREE Must-Read Books for Every AI Engineer

  A handpicked list of free reads that teach you the science, logic, and real-world side of artificial intelligence.

  https://www.kdnuggets.com/the-5-free-must-read-books-for-every-ai-engineer

• 5 Fun Data Science Projects for Absolute Beginners

  These beginner-friendly projects guide you through the full data science workflow so you can learn by building and experimenting.

  https://www.kdnuggets.com/5-fun-data-science-projects-for-absolute-beginners

• How to Learn Math for Data Science: A Roadmap for Beginners

  Confused about where to start with data science math? Learn what math concepts to learn, in what order, and how to use them in practice.

  https://www.kdnuggets.com/how-to-learn-math-for-data-science-a-roadmap-for-beginners

• From Python to AI Engineer: A Self-Study Roadmap

  A practical roadmap for Python programmers to develop the advanced skills, specialized knowledge, and engineering mindset needed to become successful AI engineers in 2025.

  https://www.kdnuggets.com/from-python-to-ai-engineer-a-self-study-roadmap

• Choosing the Right Machine Learning Algorithm: A Decision Tree Approach

  Amid so many different machine learning algorithms to choose from. This guide has been designed to help you navigate towards the right one for you, depending on your data and the problem to address.

  https://www.kdnuggets.com/choosing-the-right-machine-learning-algorithm-a-decision-tree-approach

• Getting Started With a Career in Data Science

  Breaking into data science has never been easy. In this tutorial, we’ll make your life easier by providing you with a step-by-step roadmap for data science beginners.

  https://www.kdnuggets.com/getting-started-with-a-career-in-data-science

• 10 Python One-Liners for Scikit-learn

  Stop writing extra code — these 10 one-liners will take care of 80% of your Scikit-Learn tasks!

  https://www.kdnuggets.com/10-python-one-liners-for-scikit-learn

• The Ultimate Guide to Building a Machine Learning Portfolio That Lands Jobs

  In this article, you'll learn how to create a portfolio that stands out.

  https://www.kdnuggets.com/the-ultimate-guide-to-building-a-machine-learning-portfolio-that-lands-jobs

• Math, Machine Learning & Coding Needed For LLMs

  The goal of this article is to guide you through the essential mathematical foundations, machine learning techniques, and coding practices needed to work with LLMs.

  https://www.kdnuggets.com/math-machine-learning-coding-needed-llms

• Optimizing Server Performance Through Statistical Analysis

  With millions of client-server comms occurring every second across networks, the ability to maintain optimal performance is crucial to avoiding downtime, latency, and inefficiencies that could cost a business thousands or even millions of dollars.

  https://www.kdnuggets.com/optimizing-server-performance-through-statistical-analysis

• Math Myths Busted: What Beginners Actually Need for Data Science

  Terrified of calculus but dream of being a data scientist? Breathe easy! Discover the surprising truth about math in data science and how you can succeed without being a math genius.

  https://www.kdnuggets.com/math-myths-busted-beginners-actually-need-data-science

• 7 Critical Thinking Skills Needed in Data Science

  Hone these analytical and practical thinking skills to become an authentic "data wizard".

  https://www.kdnuggets.com/7-critical-thinking-skills-needed-in-data-science

• Doing Customer Segmentation with R

  Customer segmentation involves dividing a customer base into groups with similar traits. This article will show you how to segment customers using R.

  https://www.kdnuggets.com/doing-customer-segmentation-with-r

• How to Calculate Eigenvalues and Eigenvectors with NumPy

  Eigenvalues and eigenvectors are fundamental concepts in linear algebra that may be applied to many applications, such as in revealing the stability of a system, or for dimensionality reduction.

  https://www.kdnuggets.com/how-to-calculate-eigenvalues-and-eigenvectors-with-numpy

• 7 Steps to Mastering Math for Data Science

  Want to learn math for data science? This guide will help you go about learning math for data science—linear algebra, calculus, statistics, and more.

  https://www.kdnuggets.com/7-steps-to-mastering-math-for-data-science

• Tools Every AI Engineer Should Know: A Practical Guide

  Explore essential tools and skills for AI engineers: Python, R, big data frameworks, and cloud services essential for building and optimizing AI systems.

  https://www.kdnuggets.com/tools-every-ai-engineer-should-know-a-practical-guide

• 5 Top Machine Learning Courses You Can Take in 2024

  Forget about going to university and become a machine learning professional with these 5 top certifications.

  https://www.kdnuggets.com/5-top-machine-learning-courses-you-can-take-in-2024

• Feature Engineering for Beginners

  This guide introduces some key techniques in the feature engineering process and provides practical examples in Python.

  https://www.kdnuggets.com/feature-engineering-for-beginners

• A Beginner’s Guide to the Top 10 Machine Learning Algorithms

  Data science’s essence lies in machine learning algorithms. Here are ten algorithms that are a great introduction to machine learning for any beginner!

  https://www.kdnuggets.com/a-beginner-guide-to-the-top-10-machine-learning-algorithms

• 2024 Reading List: 5 Essential Reads on Artificial Intelligence

  Transform your understanding of current and future tech with these top 5 AI reads to explore the minds shaping our future.

  https://www.kdnuggets.com/2024-reading-list-5-essential-reads-on-artificial-intelligence

• 5 Free Courses to Master MLOps

  Have you finished learning the basics of machine learning and now wondering what's next? You're in the right place!

  https://www.kdnuggets.com/5-free-courses-to-master-mlops

• 10 GitHub Repositories to Master Machine Learning

  The blog covers machine learning courses, bootcamps, books, tools, interview questions, cheat sheets, MLOps platforms, and more to master ML and secure your dream job.

  https://www.kdnuggets.com/10-github-repositories-to-master-machine-learning

• 5 Free Courses to Master Machine Learning

  Are you excited to learn about and build machine learning models? Start learning today with these free machine learning courses.

  https://www.kdnuggets.com/5-free-courses-to-master-machine-learning

• 5 Ways You Can Use ChatGPT Vision for Data Analysis

  Enhances data analysis by interpreting visual data, including math formula, data extraction, evaluating the results, dashboards, and charts.

  https://www.kdnuggets.com/5-ways-you-can-use-chatgpt-vision-for-data-analysis

• 5 Free Books to Master Machine Learning

  Machine Learning is one of the most exciting fields in computer science today. In this article, we will take a look at the five best yet free books to learn machine learning in 2023.

  https://www.kdnuggets.com/5-free-books-to-master-machine-learning

• Deploying Your Machine Learning Model to Production in the Cloud

  Learn a simple way to have a live model hosted on AWS.

  https://www.kdnuggets.com/deploying-your-ml-model-to-production-in-the-cloud

• Understanding Unsupervised Learning

  Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.

  https://www.kdnuggets.com/unveiling-unsupervised-learning

• Getting Started with Python for Data Science

  Back to Basics: A beginner's guide to setting up Python and understanding its role in data science.

  https://www.kdnuggets.com/getting-started-with-python-for-data-science

• Fundamentals Of Statistics For Data Scientists and Analysts

  Key statistical concepts for your data science or data analysis journey.

  https://www.kdnuggets.com/2023/08/fundamentals-statistics-data-scientists-analysts.html

• A Practical Approach To Feature Engineering In Machine Learning

  This article discussed the importance of feature learning in machine learning and how it can be implemented in simple, practical steps.

  https://www.kdnuggets.com/2023/07/practical-approach-feature-engineering-machine-learning.html

• Mastering NLP Job Interviews

  What is NLP, and what types of questions related to NLP can you expect at the NLP-related job interviews?

  https://www.kdnuggets.com/2022/10/nlp-interview-questions.html

• 21 Must-Have Cheat Sheets for Data Science Interviews: Unlocking Your Path to Success

  This article has researched and presents the best data science cheat sheets from around the internet, so you don’t have to do it yourself.

  https://www.kdnuggets.com/2022/06/21-cheat-sheets-data-science-interviews.html

• Advanced Feature Selection Techniques for Machine Learning Models

  Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.

  https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html

• Integrating ChatGPT Into Data Science Workflows: Tips and Best Practices

  Looking to integrate ChatGPT into your data science workflow? Here’s an example along with tips and best practices to get the most out of ChatGPT for data science.

  https://www.kdnuggets.com/2023/05/integrating-chatgpt-data-science-workflows-tips-best-practices.html

• KDnuggets News, May 17: Mojo Lang: The New Programming Language • Pandas AI: The Generative AI Python Library

  Mojo Lang: The New Programming Language • Pandas AI: The Generative AI Python Library • Data Scientist’s Guide to Cognitive Biases: A Free eBook • 8 Free AI and LLMs Playgrounds • Practical Statistics for Data Scientists

  https://www.kdnuggets.com/2023/n18.html

• A Beginner’s Guide to Anomaly Detection Techniques in Data Science

  In this article, I will give you a brief introduction to anomaly detection and I will guide you through the different techniques that you can use to identify anomalies.

  https://www.kdnuggets.com/2023/05/beginner-guide-anomaly-detection-techniques-data-science.html

• Exploring Unsupervised Learning Metrics

  Improves your data science skill arsenals with these metrics.

  https://www.kdnuggets.com/2023/04/exploring-unsupervised-learning-metrics.html

• Best Machine Learning Model For Sparse Data

  Sparse Data Survival Guide: Strategies for Success with Machine Learning.

  https://www.kdnuggets.com/2023/04/best-machine-learning-model-sparse-data.html

• Machine Learning Algorithms Explained in Less Than 1 Minute Each

  Learn about some of the most well known machine learning algorithms in less than a minute each.

  https://www.kdnuggets.com/2022/07/machine-learning-algorithms-explained-less-1-minute.html

• From Data Collection to Model Deployment: 6 Stages of a Data Science Project

  Here are 6 stages of a novel Data Science Project; From Data Collection to Model in Production, backed by research and examples.

  https://www.kdnuggets.com/2023/01/data-collection-model-deployment-6-stages-data-science-project.html

• Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

  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.

  https://www.kdnuggets.com/2020/10/data-science-minimum-10-essential-skills.html

• Top 38 Python Libraries for Data Science, Data Visualization & Machine Learning

  This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.

  https://www.kdnuggets.com/2020/11/top-python-libraries-data-science-data-visualization-machine-learning.html

• Python String Methods

  Learn Python String methods to get better at writing efficient and elegant code.

  https://www.kdnuggets.com/2022/12/python-string-methods.html

• How Much Math Do You Need in Data Science?

  There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

  https://www.kdnuggets.com/2020/06/math-data-science.html

• 10 Amazing Machine Learning Visualizations You Should Know in 2023

  Yellowbrick for creating machine learning plots with less code.

  https://www.kdnuggets.com/2022/11/10-amazing-machine-learning-visualizations-know-2023.html

• 15 Free Machine Learning and Deep Learning Books

  Check out this list of 15 FREE ebooks for learning machine learning and deep learning.

  https://www.kdnuggets.com/2022/10/15-free-machine-learning-deep-learning-books.html

• Working With Sparse Features In Machine Learning Models

  Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.

  https://www.kdnuggets.com/2021/01/sparse-features-machine-learning-models.html

• 5 Free Courses to Master Linear Algebra

  Linear Algebra is an important subfield of mathematics and forms a core foundation of machine learning algorithms. The post shares five free courses to master the concepts of linear algebra.

  https://www.kdnuggets.com/2022/10/5-free-courses-master-linear-algebra.html

• Machine Learning for Everybody!

  Who is machine learning for? Everybody!

  https://www.kdnuggets.com/2022/10/machine-learning-everybody.html

• Dimensionality Reduction Techniques in Data Science

  Dimensionality reduction techniques are basically a part of the data pre-processing step, performed before training the model.

  https://www.kdnuggets.com/2022/09/dimensionality-reduction-techniques-data-science.html

• Linear Algebra for Data Science

  In this article, we discuss the importance of linear algebra in data science and machine learning.

  https://www.kdnuggets.com/2022/07/linear-algebra-data-science.html

• Python String Processing Cheatsheet

  Try this string processing primer cheatsheet to gain an understanding of using Python to manipulate and process strings at a basic level.

  https://www.kdnuggets.com/2020/01/python-string-processing-primer.html

• KDnuggets News, June 29: 20 Basic Linux Commands for Data Science Beginners; Market Data and News: A Time Series Analysis

  20 Basic Linux Commands for Data Science Beginners; Market Data and News: A Time Series Analysis; Data Science Career: 7 Expectations vs Reality; Machine Learning Is Not Like Your Brain Part 4: The Neuron’s Limited Ability to Represent Precise Values; Comprehensive Guide to the Normal Distribution

  https://www.kdnuggets.com/2022/n26.html

• Essential Math for Data Science: Visual Introduction to Singular Value Decomposition

  This article will cover singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning.

  https://www.kdnuggets.com/2022/06/essential-math-data-science-visual-introduction-singular-value-decomposition.html

• Popular Machine Learning Algorithms

  This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.

  https://www.kdnuggets.com/2022/05/popular-machine-learning-algorithms.html

• The “Hello World” of Tensorflow

  In this article, we will build a beginner-friendly machine learning model using TensorFlow.

  https://www.kdnuggets.com/2022/05/hello-world-tensorflow.html

• 3 Steps for Harnessing the Power of Data

  Even though data is now produced at an unprecedented amount, data must be collected, processed, transformed, and analyzed to harness its power. Read more about the 3 main stages involved.

  https://www.kdnuggets.com/2022/05/3-steps-harnessing-power-data.html

• How to Generate Synthetic Tabular Dataset

  Check out this article on using CTGANs to create synthetic datasets for reducing privacy risks, training and testing machine learning models, and developing data-centric AI products.

  https://www.kdnuggets.com/2022/03/generate-tabular-synthetic-dataset.html

• Top 3 Free Resources to Learn Linear Algebra for Machine Learning

  This article will solely focus on learning linear algebra, as it forms the backbone of machine learning model implementation.

  https://www.kdnuggets.com/2022/03/top-3-free-resources-learn-linear-algebra-machine-learning.html

• An Easy Guide to Choose the Right Machine Learning Algorithm

  There's no free lunch in machine learning. So, determining which algorithm to use depends on many factors from the type of problem at hand to the type of output you are looking for. This guide offers several considerations to review when exploring the right ML approach for your dataset.

  https://www.kdnuggets.com/2020/05/guide-choose-right-machine-learning-algorithm.html

• Feature Selection: Where Science Meets Art

  From heuristic to algorithmic feature selection techniques for data science projects.

  https://www.kdnuggets.com/2021/12/feature-selection-science-meets-art.html

• Introduction to Clustering in Python with PyCaret

  A step-by-step, beginner-friendly tutorial for unsupervised clustering tasks in Python using PyCaret.

  https://www.kdnuggets.com/2021/12/introduction-clustering-python-pycaret.html

• Four Basic Steps in Data Preparation">Four Basic Steps in Data Preparation

  What we would like to do here is introduce four very basic and very general steps in data preparation for machine learning algorithms. We will describe how and why to apply such transformations within a specific example.

  https://www.kdnuggets.com/2021/10/four-basic-steps-data-preparation.html

• How to Transform Your Data in Snowflake

  Data transformation is the biggest bottleneck in the analytics workflow. The modern approach to data pipelines is ELT, or extract, transform, and load, with data transformation performed in your Snowflake data warehouse. A new breed of “no-/low-code” data transformation tools, such as Datameer, are emerging to allow the wider analytics community to transform data on their own, eliminating analytics bottlenecks.

  https://www.kdnuggets.com/2021/10/datameer-transform-data-snowflake.html

• Introduction to AutoEncoder and Variational AutoEncoder (VAE)">Introduction to AutoEncoder and Variational AutoEncoder (VAE)

  Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.

  https://www.kdnuggets.com/2021/10/introduction-autoencoder-variational-autoencoder-vae.html

• KDnuggets™ News 21:n36, Sep 22: The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python

  The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python; Introduction to Automated Machine Learning; How to be a Data Scientist without a STEM degree; What Is The Real Difference Between Data Engineers and Data Scientists?

  https://www.kdnuggets.com/2021/n36.html

• Introduction to Automated Machine Learning

  AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.

  https://www.kdnuggets.com/2021/09/introduction-automated-machine-learning.html

• Top 18 Low-Code and No-Code Machine Learning Platforms">Top 18 Low-Code and No-Code Machine Learning Platforms

  Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.

  https://www.kdnuggets.com/2021/09/top-18-low-code-no-code-machine-learning-platforms.html

• How Machine Learning Leverages Linear Algebra to Solve Data Problems

  Why you should learn the fundamentals of linear algebra.

  https://www.kdnuggets.com/2021/09/machine-learning-leverages-linear-algebra-solve-data-problems.html

• Data Science Cheat Sheet 2.0">Data Science Cheat Sheet 2.0

  Check out this helpful, 5-page data science cheat sheet to assist with your exam reviews, interview prep, and anything in-between.

  https://www.kdnuggets.com/2021/09/data-science-cheat-sheet.html

• Mastering Clustering with a Segmentation Problem

  The one stop shop for implementing the most widely used models in Python for unsupervised clustering.

  https://www.kdnuggets.com/2021/08/mastering-clustering-segmentation-problem.html

• 30 Most Asked Machine Learning Questions Answered

  There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.

  https://www.kdnuggets.com/2021/08/30-machine-learning-questions-answered.html

• This Data Visualization is the First Step for Effective Feature Selection

  Understanding the most important features to use is crucial for developing a model that performs well. Knowing which features to consider requires experimentation, and proper visualization of your data can help clarify your initial selections. The scatter pairplot is a great place to start.

  https://www.kdnuggets.com/2021/06/data-visualization-feature-selection.html

• Awesome list of datasets in 100+ categories

  With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  https://www.kdnuggets.com/2021/05/awesome-list-datasets.html

• A checklist to track your Data Science progress">A checklist to track your Data Science progress

  Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.

  https://www.kdnuggets.com/2021/05/checklist-data-science-progress.html

• Essential Linear Algebra for Data Science and Machine Learning">Essential Linear Algebra for Data Science and Machine Learning

  Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.

  https://www.kdnuggets.com/2021/05/essential-linear-algebra-data-science-machine-learning.html

• Data Science 101: Normalization, Standardization, and Regularization

  Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.

  https://www.kdnuggets.com/2021/04/data-science-101-normalization-standardization-regularization.html

• The Best Machine Learning Frameworks & Extensions for Scikit-learn">The Best Machine Learning Frameworks & Extensions for Scikit-learn

  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  https://www.kdnuggets.com/2021/03/best-machine-learning-frameworks-extensions-scikit-learn.html

• Data Science Learning Roadmap for 2021">Data Science Learning Roadmap for 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.

  https://www.kdnuggets.com/2021/02/data-science-learning-roadmap-2021.html

• Inside the Architecture Powering Data Quality Management at Uber

  Data Quality Monitor implements novel statistical methods for anomaly detection and quality management in large data infrastructures.

  https://www.kdnuggets.com/2021/02/inside-architecture-powering-data-quality-management-uber.html

• Essential Math for Data Science: Scalars and Vectors

  Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations.

  https://www.kdnuggets.com/2021/02/essential-math-data-science-scalars-vectors.html

• Getting Started with 5 Essential Natural Language Processing Libraries">Getting Started with 5 Essential Natural Language Processing Libraries

  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.

  https://www.kdnuggets.com/2021/02/getting-started-5-essential-nlp-libraries.html

• The Ultimate Scikit-Learn Machine Learning Cheatsheet">The Ultimate Scikit-Learn Machine Learning Cheatsheet

  With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

  https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions.html

• K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines">K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines

  K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.

  https://www.kdnuggets.com/2021/01/k-means-faster-lower-error-scikit-learn.html

• My Data Science Learning Journey So Far">My Data Science Learning Journey So Far

  These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.

  https://www.kdnuggets.com/2021/01/data-science-learning-journey.html

• Matrix Decomposition Decoded

  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.

  https://www.kdnuggets.com/2020/12/matrix-decomposition-decoded.html

• Data Compression via Dimensionality Reduction: 3 Main Methods

  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.

  https://www.kdnuggets.com/2020/12/data-compression-dimensionality-reduction.html

• 20 Core Data Science Concepts for Beginners">20 Core Data Science Concepts for Beginners

  With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.

  https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html

• An Introduction to AI, updated">An Introduction to AI, updated

  We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.

  https://www.kdnuggets.com/2020/10/introduction-ai-updated.html

• How to ace the data science coding challenge">How to ace the data science coding challenge

  Preparing to interview for a Data Scientist position takes preparation and practice, and then it could all boil down to a final review of your skills. Based on personal experience, these tips on how to approach such a review will help you excel in the coding challenge project for your next interview.

  https://www.kdnuggets.com/2020/10/ace-data-science-coding-challenge.html

• How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy">How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy

  Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.

  https://www.kdnuggets.com/2020/09/improve-machine-learning-models-accuracy.html

• How to Evaluate the Performance of Your Machine Learning Model">How to Evaluate the Performance of Your Machine Learning Model

  You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.

  https://www.kdnuggets.com/2020/09/performance-machine-learning-model.html

• These Data Science Skills will be your Superpower">These Data Science Skills will be your Superpower

  Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist.

  https://www.kdnuggets.com/2020/08/data-science-skills-superpower.html

• The List of Top 10 Lists in Data Science">The List of Top 10 Lists in Data Science

  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.

  https://www.kdnuggets.com/2020/08/top-10-lists-data-science.html

• Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide

  A character-level LSTM (Long short-term memory) RNN (Recurrent Neural Network) is trained on ~100k recipes dataset using TensorFlow. The model suggested the recipes "Cream Soda with Onions", "Puff Pastry Strawberry Soup", "Zucchini flavor Tea", and "Salmon Mousse of Beef and Stilton Salad with Jalapenos". Yum!? Follow along this detailed guide with code to create your own recipe-generating chef.

  https://www.kdnuggets.com/2020/07/generating-cooking-recipes-using-tensorflow.html

• Data Cleaning: The secret ingredient to the success of any Data Science Project

  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.

  https://www.kdnuggets.com/2020/07/data-cleaning-secret-ingredient-success-data-science-project.html

• How to Prepare Your Data

  This is an overview of structuring, cleaning, and enriching raw data.

  https://www.kdnuggets.com/2020/06/how-prepare-your-data.html

• Introduction to Convolutional Neural Networks

  The article focuses on explaining key components in CNN and its implementation using Keras python library.

  https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html

• Model Evaluation Metrics in Machine Learning">Model Evaluation Metrics in Machine Learning

  A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.

  https://www.kdnuggets.com/2020/05/model-evaluation-metrics-machine-learning.html

• Beginners Learning Path for Machine Learning">Beginners Learning Path for Machine Learning

  So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

  https://www.kdnuggets.com/2020/05/beginners-learning-path-machine-learning.html

• How Deep Learning is Accelerating Drug Discovery in Pharmaceuticals">How Deep Learning is Accelerating Drug Discovery in Pharmaceuticals

  The goal of this essay is to discuss meaningful machine learning progress in the real-world application of drug discovery. There’s even a solid chance of the deep learning approach to drug discovery changing lives for the better doing meaningful good in the world.

  https://www.kdnuggets.com/2020/04/deep-learning-accelerating-drug-discovery-pharmaceuticals.html

• Data Science Curriculum for self-study

  Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.

  https://www.kdnuggets.com/2020/02/data-science-curriculum-self-study.html

• Exoplanet Hunting Using Machine Learning

  Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.

  https://www.kdnuggets.com/2020/01/exoplanet-hunting-machine-learning.html

• NLP Year in Review — 2019

  In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.

  https://www.kdnuggets.com/2020/01/nlp-year-review-2019.html

• The Data Science Interview Study Guide

  Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.

  https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html

• Random Forest® — A Powerful Ensemble Learning Algorithm

  The article explains the Random Forest algorithm and how to build and optimize a Random Forest classifier.

  https://www.kdnuggets.com/2020/01/random-forest-powerful-ensemble-learning-algorithm.html

• Survey Segmentation Tutorial

  Learn the basics of verifying segmentation, analyzing the data, and creating segments in this tutorial. When reviewing survey data, you will typically be handed Likert questions (e.g., on a scale of 1 to 5), and by using a few techniques, you can verify the quality of the survey and start grouping respondents into populations.

  https://www.kdnuggets.com/2020/01/survey-segmentation-tutorial.html

• Beginner’s Guide to K-Nearest Neighbors in R: from Zero to Hero

  This post presents a pipeline of building a KNN model in R with various measurement metrics.

  https://www.kdnuggets.com/2020/01/beginners-guide-nearest-neighbors-r.html

• Fighting Overfitting in Deep Learning

  This post outlines an attack plan for fighting overfitting in neural networks.

  https://www.kdnuggets.com/2019/12/fighting-overfitting-deep-learning.html

• Interpretability part 3: opening the black box with LIME and SHAP

  The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.

  https://www.kdnuggets.com/2019/12/interpretability-part-3-lime-shap.html

• Understanding NLP and Topic Modeling Part 1

  In this post, we seek to understand why topic modeling is important and how it helps us as data scientists.

  https://www.kdnuggets.com/2019/11/understanding-nlp-topic-modeling-part-1.html

• Research Guide for Transformers

  The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

  https://www.kdnuggets.com/2019/10/research-guide-transformers.html

• Anomaly Detection, A Key Task for AI and Machine Learning, Explained

  One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.

  https://www.kdnuggets.com/2019/10/anomaly-detection-explained.html

• How to Become a (Good) Data Scientist – Beginner Guide">How to Become a (Good) Data Scientist – Beginner Guide

  A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.

  https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.html

• Clustering Metrics Better Than the Elbow Method

  We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.

  https://www.kdnuggets.com/2019/10/clustering-metrics-better-elbow-method.html

• Customer Segmentation for R Users

  This article shows you how to separate your customers into distinct groups based on their purchase behavior. For the R enthusiasts out there, I demonstrated what you can do with r/stats, ggradar, ggplot2, animation, and factoextra.

  https://www.kdnuggets.com/2019/09/customer-segmentation-r-users.html