Consider the following example. You run a news website which has its revenue tied to the number of users…
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Overcoming Obstacles to Machine Learning Adoption
Download the full report. Our friends over a H2O. ai have sponsored a new Business Impact Brief from 451 Research…
Continue ReadingHow to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark
Fundamentals of Spark Streaming Spark Streaming is an extension of the core Spark API that enables scalable and fault-tolerant…
Continue ReadingTune Hyperparameters for Classification Machine Learning Algorithms
Last Updated on December 13, 2019Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm…
Continue ReadingScalable graph machine learning: a mountain we can climb?
By Kevin Jung, Software Engineer at CSIRO Data61. Graph machine learning is still a relatively new and developing area of…
Continue ReadingAI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020
Reports indicate a 1 in 10 success rate. Not great. So AutoML will be in demand in 2020, although I…
Continue ReadingQeexo AutoML Demo: Automating Machine Learning for Embedded Devices
Qeexo spun out of Carnegie Mellon University, has for a long time developed multi-touch technology for handset manufacturers which does…
Continue ReadingHow to Use Out-of-Fold Predictions in Machine Learning
Last Updated on December 6, 2019Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the…
Continue ReadingAzure 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 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 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 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,…
Continue ReadingActiveWizards: machine learning company
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…
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