- 6 Predictive Models Every Beginner Data Scientist Should Master - Dec 23, 2021.
Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
Linear Regression, Logistic Regression, Machine Learning, random forests algorithm
- How to Select an Initial Model for your Data Science Problem - Aug 20, 2021.
Save yourself some time and headaches and start simple.
Data Science, Linear Regression, Logistic Regression, Modeling
- 10 Statistical Concepts You Should Know For Data Science Interviews - Feb 23, 2021.
Data Science is founded on time-honored concepts from statistics and probability theory. Having a strong understanding of the ten ideas and techniques highlighted here is key to your career in the field, and also a favorite topic for concept checks during interviews.
Bayes Theorem, Interview Questions, Linear Regression, Logistic Regression, P-value, Sampling, Statistics
- Machine Learning – it’s all about assumptions - Feb 11, 2021.
Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.
Algorithms, Decision Trees, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, SVM, XGBoost
- Popular Machine Learning Interview Questions, part 2 - Jan 27, 2021.
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
Convolutional Neural Networks, Interview Questions, Linear Regression, Logistic Regression, Machine Learning, Regularization, Transfer Learning, Unbalanced
- 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.
Classification, Explained, Linear Regression, Logistic Regression, Probability
- How to Build Your Own Logistic Regression Model in Python - Oct 31, 2019.
A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.
Logistic Regression, Machine Learning, Python
- Common Machine Learning Obstacles - Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.
Cross-validation, Decision Trees, Logistic Regression, Machine Learning, MathWorks, Overfitting, SVM
- A Gentle Introduction to Noise Contrastive Estimation - Jul 25, 2019.
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.
Deep Learning, Logistic Regression, Neural Networks, Noise, Random, Sampling, word2vec
- Logistic Regression: A Concise Technical Overview - Jan 23, 2019.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
Logistic Regression, Machine Learning
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
Decision Trees, Deep Learning, Linear Regression, Logistic Regression, Machine Learning, Neural Networks, SVM
- Multi-Class Text Classification with Doc2Vec & Logistic Regression - Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
Logistic Regression, NLP, Python, Text Classification
- 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - May 8, 2018.
Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.
Data Scientist, Logistic Regression, Machine Learning
- 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
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks
- 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.
Pages: 1 2
Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10
- 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
- Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
Algorithms, Dataiku, Decision Trees, Gradient Boosting, Linear Regression, Logistic Regression, random forests algorithm
- What is the Role of the Activation Function in a Neural Network? - Aug 30, 2016.
Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
Linear Regression, Logistic Regression, Neural Networks
- 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
- Using Ensembles in Kaggle Data Science Competitions- Part 3 - Jun 27, 2015.
Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.
Competition, Data blending, Kaggle, Logistic Regression, Predictive Models