- Introduction to Binary Classification with PyCaret - Dec 7, 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 it for binary classification.
Classification, Machine Learning, PyCaret, Python
- Visual Scoring Techniques for Classification Models - Nov 3, 2021.
Read this article assessing a model performance in a broader context.
Classification, Knime, Low-Code, Machine Learning, Metrics, Visualization
Teaching AI to Classify Time-series Patterns with Synthetic Data - Oct 1, 2021.
How to build and train an AI model to identify various common anomaly patterns in time-series data.
AI, Classification, Python, Synthetic Data, Time Series
- How To Deal With Imbalanced Classification, Without Re-balancing the Data - Sep 23, 2021.
Before considering oversampling your skewed data, try adjusting your classification decision threshold, in Python.
Balancing Classes, Classification, Python, Unbalanced
- Binary Classification with Automated Machine Learning - May 17, 2021.
Check out how to use the open-source MLJAR auto-ML to build accurate models faster.
Automated Machine Learning, AutoML, Classification, Open Source
- Metric Matters, Part 1: Evaluating Classification Models - Mar 16, 2021.
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification models.
Accuracy, Classification, Metrics, Precision, Recall, ROC-AUC
- Support Vector Machine for Hand Written Alphabet Recognition in R - Jan 27, 2021.
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
Classification, Image Recognition, Machine Learning, R, Support Vector Machines
- Undersampling Will Change the Base Rates of Your Model’s Predictions - Dec 17, 2020.
In classification problems, the proportion of cases in each class largely determines the base rate of the predictions produced by the model. Therefore if you use sampling techniques that change this proportion, there is a good chance you will want to rescale / calibrate your predictions before using them in the wild.
Classification, Modeling, Predictions, R, Sampling
- 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
- Essential Data Science Tips: How to Use One-Vs-Rest and One-Vs-One for Multi-Class Classification - Aug 6, 2020.
Classification, as a predictive model, involves aligning each class label to examples. Algorithms designed for binary classification cannot be applied to multi-class classification problems. For such situations, heuristic methods come in handy.
Classification, Machine Learning
- Spam Filter in Python: Naive Bayes from Scratch - Jul 8, 2020.
In this blog post, learn how to build a spam filter using Python and the multinomial Naive Bayes algorithm, with a goal of classifying messages with a greater than 80% accuracy.
Classification, Naive Bayes, Python, Text Classification
- A Classification Project in Machine Learning: a gentle step-by-step guide - Jun 17, 2020.
Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.
Beginners, Classification, Machine Learning
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
- A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.
Classification, Classifier, Interpretability, Machine Learning, Metrics, ROC-AUC
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
- Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
Which algorithm works best for unbalanced data? Are there any tradeoffs?
Algorithms, Classification, Machine Learning, R, ROC-AUC, Unbalanced
- 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
- Reddit Post Classification - Sep 18, 2019.
This article covers the implementation of a data scraping and natural language processing project which had two parts: scrape as many posts from Reddit’s API as allowed &then use classification models to predict the origin of the posts.
Classification, NLP, Reddit
- Understanding Decision Trees for Classification in Python - Aug 21, 2019.
This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.
Classification, Decision Trees, Python, scikit-learn
7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning
- Using Caret in R to Classify Term Deposit Subscriptions for a Bank - Feb 4, 2019.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this.
Banking, Classification, R
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
The Essence of Machine Learning - Dec 28, 2018.
And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.
Aaron Courville, Classification, Ian Goodfellow, Machine Learning, Tom Mitchell, Yoshua Bengio
- Synthetic Data Generation: A must-have skill for new data scientists - Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
Pages: 1 2
Classification, Clustering, Datasets, Machine Learning, Python, Synthetic Data
- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
Pages: 1 2
Classification, Computer Vision, Image Recognition, Keras, Python
- Unfolding Naive Bayes From Scratch - Sep 25, 2018.
Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!
Pages: 1 2
Bayesian, Classification, Naive Bayes, Probability, Statistics
AI Knowledge Map: How To Classify AI Technologies - Aug 31, 2018.
What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.
AI, Classification, Deep Learning, Machine Intelligence, Machine Learning, Neural Networks
- Understanding What is Behind Sentiment Analysis – Part 2 - Apr 20, 2018.
Fine-tuning our sentiment classifier...
Classification, NLP, Sentiment Analysis
- Understanding What is Behind Sentiment Analysis – Part 1 - Apr 13, 2018.
Build your first sentiment classifier in 3 steps.
Classification, NLP, Sentiment Analysis
- Using Tensorflow Object Detection to do Pixel Wise Classification - Mar 29, 2018.
Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?
Classification, Image Recognition, Object Detection, Python, TensorFlow
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.
Classification, Explained, Sentiment Analysis
- Hierarchical Classification – a useful approach for predicting thousands of possible categories - Mar 12, 2018.
A detailed look at the flat and hierarchical classification approach to dealing with multi-class classification problems.
Classification, Clustering, John Snow Labs
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
- 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
- The Machine Learning Abstracts: Classification - Jul 27, 2017.
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
Algorithms, Classification, Machine Learning
- 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
7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
Pages: 1 2
7 Steps, Classification, Clustering, Deep Learning, Ensemble Methods, Gradient Boosting, Machine Learning, Python, scikit-learn, Sebastian Raschka
- What I Learned Implementing a Classifier from Scratch in Python - Feb 28, 2017.
In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.
Classification, Machine Learning, Perceptron, Python, Sebastian Raschka
- 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
Pages: 1 2
Anomaly Detection, Bias, Classification, Data Science, Donald Trump, Interview Questions, Outliers, Overfitting, Variance
- The Costs of Misclassifications - Dec 14, 2016.
Importance of correct classification and hazards of misclassification are subjective or we can say varies on case-to-case. Lets see how cost of misclassification is measured from monetary perspective.
Accuracy, Classification, Cost Sensitive, Salford Systems
- 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
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
Pages: 1 2
Accuracy, Classification, CleverTap, Measurement, Metrics, Precision, Unbalanced
- Random Forests® in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
Algorithms, Classification, Ensemble Methods, Python, random forests algorithm, Yhat
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
Pages: 1 2
Beginners, Classification, Clustering, Machine Learning, Pandas, Python, R, scikit-learn, Software Developer
- Artificial Intelligence Classification Matrix - Nov 3, 2016.
There might be several different ways to think around machine intelligence startups; too narrow of a framework might be counterproductive given the flexibility of the sector and the facility of transitioning from one group to another. Check out this categorization matrix.
AI, Artificial Intelligence, Classification, Startups
- MLDB: The Machine Learning Database - Oct 17, 2016.
MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
Classification, Database, Machine Learning, TensorFlow, Transfer Learning
- Improving Nudity Detection and NSFW Image Recognition - Jun 25, 2016.
This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?
Algorithmia, Algorithms, Classification
- 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
- Amazon Machine Learning: Nice and Easy or Overly Simple? - Feb 17, 2016.
Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.
Amazon, Classification, Machine Learning, MLaaS
- 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
- 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
- Book: Data Classification: Algorithms and Applications - Aug 2, 2014.
Learn a wide variety of data classification techniques and their methods, domains, and variations in this comprehensive survey of the area of data classification.
Algorithms, Book, Charu Aggarwal, Classification, CRC Press
