- 11 Most Practical Data Science Skills for 2022 - Oct 19, 2021.
While the field of data science continues to evolve with exciting new progress in analytical approaches and machine learning, there remain a core set of skills that are foundational for all general practitioners and specialists, especially those who want to be employable with full-stack capabilities.
Career Advice, Data Science Skills, Explainable AI, Feature Engineering, GitHub, NLP, Regression, SQL
- How causal inference lifts augmented analytics beyond flatland - Aug 27, 2021.
In our quest to better understand and predict business outcomes, traditional predictive modeling tends to fall flat. However, causal inference techniques along with business analytics approaches can unravel what truly changes your KPIs.
Analytics, Causality, Data Science, Python, Regression
- 30 Most Asked Machine Learning Questions Answered - Aug 3, 2021.
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.
Beginners, Interview Questions, Machine Learning, Regression, scikit-learn
- 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.
Machine Learning, PyCaret, Python, Regression, Time Series
- Data Science 101: Normalization, Standardization, and Regularization - Apr 20, 2021.
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.
Data Preprocessing, Feature Engineering, Normalization, Regression, Regularization, Statistics
- Learning from machine learning mistakes - Mar 19, 2021.
Read this article and discover how to find weak spots of a regression model.
Machine Learning, Mistakes, Modeling, Regression
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.
Algorithms, Decision Trees, Explained, Gradient Boosting, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM
- Simple & Intuitive Ensemble Learning in R - Dec 2, 2020.
Read about metaEnsembleR, an R package for heterogeneous ensemble meta-learning (classification and regression) that is fully-automated.
Classification, Ensemble Methods, R, Regression
- 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.
Data Visualization, Metrics, Modeling, Python, Regression
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.
Data Preparation, Data Science, GitHub, Portfolio, Python, Regression, Salary
How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
Algorithms, Decision Trees, Interview Questions, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM
- R squared Does Not Measure Predictive Capacity or Statistical Adequacy - Jul 31, 2020.
The fact that R-squared shouldn't be used for deciding if you have an adequate model is counter-intuitive and is rarely explained clearly. This demonstration overviews how R-squared goodness-of-fit works in regression analysis and correlations, while showing why it is not a measure of statistical adequacy, so should not suggest anything about future predictive performance.
Predictive Analytics, Regression, Statistics
- A Complete Guide To Survival Analysis In Python, part 3 - Jul 30, 2020.
Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
Jupyter, Python, Regression, Statistics, Survival Analysis
Model Evaluation Metrics in Machine Learning - May 28, 2020.
A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
Classification, Confusion Matrix, Machine Learning, Metrics, Python, Regression
- Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.
Beginners, Classification, Machine Learning, Python, Regression, scikit-learn, Supervised Learning, Unsupervised Learning
- Designing Your Neural Networks - Nov 4, 2019.
Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.
Beginners, Classification, Dropout, Gradient Descent, Neural Networks, Regression
- How Bad is Multicollinearity? - Sep 17, 2019.
For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.
Analytics, Multicollinearity, Regression, Statistics
- From Data Pre-processing to Optimizing a Regression Model Performance - Jul 19, 2019.
All you need to know about data pre-processing, and how to build and optimize a regression model using Backward Elimination method in Python.
Model Performance, Modeling, Optimization, Regression
- How do you check the quality of your regression model in Python? - Jul 2, 2019.
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.
Data Science, Multicollinearity, Python, Regression, Statistics
- Separating signal from noise - Jun 4, 2019.
When we are building a model, we are making the assumption that our data has two parts, signal and noise. Signal is the real pattern, the repeatable process that we hope to capture and describe. The noise is everything else that gets in the way of that.
Noise, Regression, Statistics, Time Series
- Choosing Between Model Candidates - May 29, 2019.
Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.
Data Science, Modeling, Regression, Time Series
Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression
7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition - Jan 29, 2019.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
7 Steps, Classification, Clustering, Jupyter, Machine Learning, Python, Regression
- Autoregressive Models in TensorFlow - Aug 6, 2018.
This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations.
Regression, TensorFlow, Time Series
- Selecting the Best Machine Learning Algorithm for Your Regression Problem - Aug 1, 2018.
This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!
Algorithms, Machine Learning, Regression
- Deep Quantile Regression - Jul 3, 2018.
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.
Deep Learning, Hyperparameter, Keras, Neural Networks, Python, Regression
- Data Science Predicting The Future - Jun 19, 2018.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
Data Science, Forecasting, Machine Learning, Programming Languages, Regression
Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
Pages: 1 2
Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM
Logistic Regression: A Concise Technical Overview - Feb 16, 2018.
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
Algorithms, Classification, Logistic Regression, Machine Learning, Regression
- Which Machine Learning Algorithm be used in year 2118? - Feb 9, 2018.
So what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.
Algorithms, Machine Learning, Regression, Trends
- Topological Data Analysis for Data Professionals: Beyond Ayasdi - Jan 16, 2018.
We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.
Algorithms, Clustering, R, Regression, Topological Data Analysis
- How Bayesian Networks Are Superior in Understanding Effects of Variables - Nov 9, 2017.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
Bayesian, Bayesian Networks, Predictive Models, Probability, Regression, Statistics
- 3 different types of machine learning - Nov 1, 2017.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
Pages: 1 2
Classification, Clustering, Machine Learning, Regression, Reinforcement Learning, Supervised Learning
- Top 6 errors novice machine learning engineers make - Oct 30, 2017.
What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.
Beginners, Machine Learning, Mistakes, Outliers, Regression, Regularization, Time Series
- Learn Generalized Linear Models (GLM) using R - Oct 11, 2017.
In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.
Pages: 1 2
Generalized Linear Models, Linear Regression, Logistic Regression, Machine Learning, R, Regression
Is Regression Analysis Really Machine Learning? - Jun 5, 2017.
What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?
Applied Statistics, Linear Regression, Machine Learning, Regression, Statistics
- Machine Learning Crash Course: Part 1 - May 24, 2017.
This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.
Classification, Cost Function, Gradient Descent, Machine Learning, Regression
- The Data Science of Steel, or Data Factory to Help Steel Factory - Apr 25, 2017.
Applying Machine Learning to steel production is really hard! Here are some lessons from Yandex researchers on how to balance the need for findings to be accurate, useful, and understandable at the same time.
Applications, Recommendation Engine, Regression, Russia, Steel, Yandex
- Building Regression Models in R using Support Vector Regression - Mar 8, 2017.
The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.
R, Regression, Support Vector Machines
- Regression Analysis: A Primer - Feb 6, 2017.
Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.
Applied Statistics, Linear Regression, Regression
- Data Science Basics: What Types of Patterns Can Be Mined From Data? - Dec 14, 2016.
Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.
Beginners, Classification, Data Science, Frequent Pattern Mining, Outliers, Regression
- Data Analytics Models in Quantitative Finance and Risk Management - Dec 13, 2016.
We review how key data science algorithms, such as regression, feature selection, and Monte Carlo, are used in financial instrument pricing and risk management.
Data Analytics, Feature Selection, Finance, Regression, Risk Modeling
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization
Top Algorithms and Methods Used by Data Scientists - Sep 12, 2016.
Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
Pages: 1 2
Algorithms, Clustering, Data Visualization, Decision Trees, Poll, Regression
- A Neat Trick to Increase Robustness of Regression Models - Aug 22, 2016.
Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?
CleverTap, Linear Regression, Outliers, Overfitting, Regression
- What Statistics Topics are Needed for Excelling at Data Science? - Aug 2, 2016.
Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.
Bayesian, Distribution, Machine Learning, Markov Chains, Probability, Regression, Statistics
- What is Softmax Regression and How is it Related to Logistic Regression? - Jul 1, 2016.
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
Logistic Regression, Machine Learning, Regression
- Regularization in Logistic Regression: Better Fit and Better Generalization? - Jun 24, 2016.
A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
Cost Function, Logistic Regression, Machine Learning, Regression, Regularization
- Regression & Correlation for Military Promotion: A Tutorial - Apr 13, 2016.
A clear and well-written tutorial covering the concepts of regression and correlation, focusing on military commander promotion as a use case.
Pages: 1 2
Algobeans, Correlation, Military, Regression
- Salford Predictive Modeler 8: Faster. More Machine Learning. Better results - Apr 4, 2016.
Take a giant step forward with SPM 8: Download and try it for yourself just released version 8 and get better results.
Classification, Data Science Platform, Decision Trees, Regression, Salford Systems, TreeNet
- New Salford Predictive Modeler 8 - Mar 1, 2016.
Salford Predictive Modeler software suite: Faster. More Comprehensive Machine Learning. More Automation. Better results. Take a giant step forward in your data science productivity with SPM 8. Download and try it today!
Data Science Platform, Decision Trees, Gradient Boosting, Predictive Modeler, Regression, Salford Systems
- What questions can data science answer? - Jan 1, 2016.
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.
Pages: 1 2
Classification, Clustering, Machine Learning, Regression
- 7 common mistakes when doing Machine Learning - Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
Pages: 1 2
Machine Learning, Mistakes, Overfitting, Regression, SVM
- Fundamental methods of Data Science: Classification, Regression And Similarity Matching - Jan 12, 2015.
Data classification, regression, and similarity matching underpin many of the fundamental algorithms in data science to solve business problems like consumer response prediction and product recommendation.
Classification, Data Science, Regression, Similarity
