# Explained (62)

**11 Important Probability Distributions Explained**- Jul 20, 2021.

There are many distribution functions considered in statistics and machine learning, which can seem daunting to understand at first. Many are actually closely related, and with these intuitive explanations of the most important probability distributions, you can begin to appreciate the observations of data these distributions communicate.**Data Careers in Demand: Crowd Solutions Architect Explained**- Jun 23, 2021.

How can crowdsourcing support the applications of data teams at an organization? With an ever-increasing demand for more and higher quality data, a new role of the Crowd Solutions Architect (CSA) can leverage the potential of the masses to bring an advantage to a business's capability to deliver effective AI-driven solutions.**Zero-Shot Learning: Can you classify an object without seeing it before?**- Apr 12, 2021.

Developing machine learning models that can perform predictive functions on data it has never seen before has become an important research area called zero-shot learning. We tend to be pretty great at recognizing things in the world we never saw before, and zero-shot learning offers a possible path toward mimicking this powerful human capability.**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.**Data Science vs Business Intelligence, Explained**- Feb 10, 2021.

Knowing the differences between the business intelligence and data science is more than just a matter of semantics.**Attention mechanism in Deep Learning, Explained**- Jan 11, 2021.

Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.**All Machine Learning Algorithms You Should Know in 2021**- Jan 4, 2021.

Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.**Key Data Science Algorithms Explained: From k-means to k-medoids clustering**- Dec 29, 2020.

As a core method in the Data Scientist's toolbox, k-means clustering is valuable but can be limited based on the structure of the data. Can expanded methods like PAM (partitioning around medoids), CLARA, and CLARANS provide better solutions, and what is the future of these algorithms?**Object-Oriented Programming Explained Simply for Data Scientists**- Dec 1, 2020.

Read this simple but effective guide to start using Classes in Python 3.**A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM)**- Mar 18, 2020.

Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.**Linear to Logistic Regression, Explained Step by Step**- Mar 3, 2020.

Logistic Regression is a core supervised learning technique for solving classification problems. This article goes beyond its simple code to first understand the concepts behind the approach, and how it all emerges from the more basic technique of Linear Regression.**What is Data Science?**- Nov 8, 2019.

Data Science is pitched as a modern and exciting job offering high satisfaction. Does its reality really live up to the hype? Here, we show what it's really like to work as a Data Scientist.**Anomaly Detection, A Key Task for AI and Machine Learning, Explained**- Oct 21, 2019.

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.**A Friendly Introduction to Support Vector Machines**- Sep 12, 2019.

This article explains the Support Vector Machines (SVM) algorithm in an easy way.**What’s the difference between analytics and statistics?**- Sep 6, 2019.

From asking the best questions about data to answering those questions with certainty, understanding the value of these two seemingly different professions is clarified when you see how they should work together.**Keras Callbacks Explained In Three Minutes**- Aug 9, 2019.

A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.**Deep Learning for NLP: ANNs, RNNs and LSTMs explained!**- Aug 7, 2019.

Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!**4 Most Popular Alternative Data Sources Explained**- Jul 2, 2019.

Alternative data is the new game changer. To start with alternative data, people might even wonder from where you can get hold of alternative data that can give such a competitive advantage. This post details 4 alternative data sources that you can exploit to the fullest.**The Machine Learning Puzzle, Explained**- Jun 17, 2019.

Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.**How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms**- Apr 16, 2019.

We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example.**Explaining Random Forest® (with Python Implementation)**- Mar 29, 2019.

We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.**Decision Trees — An Intuitive Introduction**- Feb 14, 2019.

An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.**Neural Networks – an Intuition**- Feb 7, 2019.

Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.**Random forests® explained intuitively**- Jan 30, 2019.

A detailed explanation of random forests, with real life use cases, a discussion into when a random forest is a poor choice relative to other algorithms, and looking at some of the advantages of using random forest.**The Backpropagation Algorithm Demystified**- Jan 2, 2019.

A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.**BERT: State of the Art NLP Model, Explained**- Dec 26, 2018.

BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.**Recent Advances for a Better Understanding of Deep Learning**- Oct 1, 2018.

A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.**Power Laws in Deep Learning 2: Universality**- Sep 26, 2018.

It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.**Power Laws in Deep Learning**- Sep 20, 2018.

In pretrained, production quality DNNs, the weight matrices for the Fully Connected (FC ) layers display Fat Tailed Power Law behavior.**5 Things You Need to Know about Sentiment Analysis and Classification**- Mar 23, 2018.

We take a look at the important things you need to know about sentiment analysis, including social media, classification, evaluation metrics and how to visualise the results.**Blockchain Key Terms, Explained**- Nov 3, 2017.

Need a quick glance over some important definitions associated with the Blockchain? Then consider this article your Blockchain Definitions 101!**XGBoost: A Concise Technical Overview**- Oct 27, 2017.

Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.**Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation**- Oct 25, 2017.

In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.

**Random Forests®, Explained**- Oct 17, 2017.

Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.**XGBoost, a Top Machine Learning Method on Kaggle, Explained**- Oct 3, 2017.

Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.**Neural Network Foundations, Explained: Activation Function**- Sep 13, 2017.

This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.**277 Data Science Key Terms, Explained**- Sep 1, 2017.

This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.**AI and Deep Learning, Explained Simply**- Jul 21, 2017.

AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.

**Machine Learning Applied to Big Data, Explained**- Jul 17, 2017.

Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.**Top KDnuggets tweets, Jun 21-27: An Introduction to Key #DataScience Concepts; Emerging #BigData #DeepLearning #Python Ecosystem**- Jun 28, 2017.

Also 5 #EBooks to Read Before Getting into #DataScience; Awesome Public Datasets on GitHub; The Data Science Process, Rediscovered.**75 Big Data Terms to Know to Make your Dad Proud**- Jun 19, 2017.

Here is a good list of 75 Big Data terms you can use to impress your father, even if you already bought him a gift.**5 Career Paths in Big Data and Data Science, Explained**- Feb 6, 2017.

Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.**Bayesian Basics, Explained**- Dec 9, 2016.

This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics and how it differs from the ordinary statistics most of us learned in college.**Data Science and Big Data, Explained**- Nov 14, 2016.

This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.**Clustering Key Terms, Explained**- Oct 18, 2016.

Getting started with Data Science or need a refresher? Clustering is among the most used tools of Data Scientists. Check out these 10 Clustering-related terms and their concise definitions.**Artificial Intelligence, Deep Learning, and Neural Networks, Explained**- Oct 14, 2016.

This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.**Adversarial Validation, Explained**- Oct 7, 2016.

This post proposes and outlines adversarial validation, a method for selecting training examples most similar to test examples and using them as a validation set, and provides a practical scenario for its usefulness.**Beginner’s Guide to Apache Flink – 12 Key Terms, Explained**- Oct 4, 2016.

We review 12 core Apache Flink concepts, to better understand what it does and how it works, including streaming engine terminology.**9 Key Deep Learning Papers, Explained**- Sep 20, 2016.

If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.**Misinformation Key Terms, Explained**- Aug 20, 2016.

Misinformation has emerged as a key issue for social media platforms. This post will introduce the concept of misinformation and the 8 Key Terms, which provides insights into mining misinformation in social media.**Big Data Key Terms, Explained**- Aug 11, 2016.

Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.**Internet of Things Key Terms, Explained**- Jul 27, 2016.

This post will define 12 Key Terms for the Internet of Things, in straightforward manner.**Predictive Analytics Introductory Key Terms, Explained**- Jul 18, 2016.

Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.**Bayesian Machine Learning, Explained**- Jul 13, 2016.

Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.

**Apache Spark Key Terms, Explained**- Jun 13, 2016.

An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.**Cloud Computing Key Terms, Explained**- Jun 9, 2016.

A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.**Hadoop Key Terms, Explained**- May 30, 2016.

An straightforward overview of 16 core Hadoop ecosystem concepts. No Big Picture discussion, just the facts.**The Data Science Puzzle, Explained**- Mar 10, 2016.

The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.**5 More arXiv Deep Learning Papers, Explained**- Jan 5, 2016.

Top recent deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.**Top 5 arXiv Deep Learning Papers, Explained**- Oct 1, 2015.

Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.**Top 10 Data Mining Algorithms, Explained**- May 21, 2015.

Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.