Last Updated on December 6, 2019Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the…
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Azure Databricks Highlights Adoption of Delta Lake, MLflow, and Integration with Azure Machine Learning at Microsoft Ignite 2019
It was an action-packed week of making new connections and learning about new innovation across data science, data engineering, and…
Continue ReadingSupercomputers and Machine Learning: A Perfect Match
To understand High-Performance (HPC) you need to first understand supercomputers. A supercomputer is a type of HPC solution which performs…
Continue ReadingWhat Does Stochastic Mean in Machine Learning?
Last Updated on November 18, 2019The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic…
Continue ReadingA Gentle Introduction to Model Selection for Machine Learning
Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on…
Continue ReadingHow to Choose a Feature Selection Method For Machine Learning
Last Updated on November 28, 2019Feature selection is the process of reducing the number of input variables when developing a…
Continue ReadingHow to Connect Model Input Data With Predictions for Machine Learning
Fitting a model to a training dataset is so easy today with libraries like scikit-learn. A model can be fit…
Continue ReadingHow to Save a NumPy Array to File for Machine Learning
Last Updated on November 13, 2019Developing machine learning models in Python often requires the use of NumPy arrays. NumPy arrays…
Continue ReadingWant to Build Machine Learning Pipelines? A Quick Introduction using PySpark
Here’s the caveat – Spark’s OneHotEncoder does not directly encode the categorical variable. First, we need to use the String…
Continue ReadingA Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values!
Now, if we talk in terms of Game Theory, the “game” here is the prediction task for a single instance…
Continue ReadingTwo Years In The Life of AI, Machine Learning, Deep Learning and Java
Which libraries and frameworks to use? Another confession, I didn’t spend too much time trying to gather and categorise these topics,…
Continue ReadingA Doomed Marriage of Machine Learning and Agile
I will never forget this date. I love success stories about ML. But, let’s face the truth: most ML…
Continue ReadingHow I Got Better at Machine Learning
This means that if you know these fundamentals, it will be much easier and less time-consuming for you to learn…
Continue ReadingStop explaining black box machine learning models for high stakes decisions and use interpretable models instead
The paper is a mix of technical and philosophical arguments and comes with two main takeaways for me: firstly, a…
Continue Reading14 Types of Learning in Machine Learning
Let me know in the comments below. First, we will take a closer look at three main types of learning…
Continue ReadingA Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically,…
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Data Science for Managers: Programming LanguagesTop 8 Data Science Use Cases in SupportActually the unlocking of hidden benefits and true potential…
Continue Reading9 Practical Actions to Improve Machine Learning for Fraud Prevention
Start with simpler, more transparent, and explainable and bias-free models and graduate to complicated models over time. MODELS – Experiment…
Continue ReadingWhy is Machine Learning Deployment Hard?
By Alexandre Gonfalonieri, AI Consultant. After several AI projects, I realized that deploying Machine Learning (ML) models at scale is…
Continue ReadingScaling Hyperopt to Tune Machine Learning Models in Python
Hyperopt is an open-source hyperparameter tuning library written for Python. With 445,000+ PyPI downloads each month and 3800+ stars…
Continue ReadingA Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There…
Continue ReadingA Gentle Introduction to Cross-Entropy for Machine Learning
Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…
Continue ReadingBest of arXiv.org for AI, Machine Learning, and Deep Learning – September 2019
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch Since the recent advent of deep reinforcement learning for…
Continue ReadingInformation Gain and Mutual Information for Machine Learning
Information gain calculates the reduction in entropy or surprise from transforming a dataset in some way. It is commonly used…
Continue ReadingMathematics behind Machine Learning – The Core Concepts you Need to Know
Well, that’s what we will learn in this article. We’ll discuss the various mathematical aspects you need to know to…
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