Top 8 Docker Images for Data Science

March 1, 2019 | 0 Comments

Dockerizing Data Science: Introduction PreReqs: Docker, images, and containers Dockerizing data science packages have become more relevant these days mainly because you can isolate your data science projects without breaking anything. Dockerizing data science projects also make most of your projects portable and sharable and not worrying about installing right ... [...Read more...]

Top 8 Docker Images for Data Science

March 1, 2019 | 0 Comments

Dockerizing Data Science: Introduction PreReqs: Docker, images, and containers Dockerizing data science packages have become more relevant these days mainly because you can isolate your data science projects without breaking anything. Dockerizing data science projects also make most of your projects portable and sharable and not worrying about installing right ... [...Read more...]

Gartner’s 2019 Take on Data Science Software

February 26, 2019 | 0 Comments

I've just updated The Popularity of Data Science Software to reflect my take on Gartner's 2019 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging through all 40+ pages of my report, here's just the updated section: Continue reading →
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Data Exploration Case Study: Credit Default

February 21, 2019 | 0 Comments

Exploratory data analysis is the main task of a Data Scientist with as much as 60% of their time being devoted to this task. As such, the majority of their time is spent on something that is rather boring compared to building models. This post will provide a simple example of ...
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RANSAC Regression in Python

February 7, 2019 | 0 Comments

RANSAC is an acronym for Random Sample Consensus. What this algorithm does is fit a regression model on a subset of data that the algorithm judges as inliers while removing outliers. This naturally improves the fit of the model due to the removal of some data points. The process that ...
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Combining Algorithms for Classification with Python

January 20, 2019 | 0 Comments

Many approaches in machine learning involve making many models that combine their strength and weaknesses to make more accuracy classification. Generally, when this is done it is the same algorithm being used. For example, random forest is simply many decision trees being developed. Even when bagging or boosting is being ...
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Gradient Boosting Regression in Python

January 13, 2019 | 0 Comments

In this  post, we will take a look at gradient boosting for regression. Gradient boosting simply makes sequential models that try to explain any examples that had not been explained by previously models. This approach makes gradient boosting superior to AdaBoost. Regression trees are mostly commonly teamed with boosting. There ...
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Gradient Boosting Classification in Python

January 8, 2019 | 0 Comments

Gradient Boosting is an alternative form of boosting to AdaBoost. Many consider gradient boosting to be a better performer than adaboost. Some differences between the two algorithms is that gradient boosting uses optimization for weight the estimators. Like adaboost, gradient boosting can be used for most algorithms but is commonly ...
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AdaBoost Regression with Python

January 6, 2019 | 0 Comments

This post will share how to use the adaBoost algorithm for regression in Python. What boosting does is that it makes multiple models in a sequential manner. Each newer model tries to successful predict what older models struggled with. For regression, the average of the models are used for the ...
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AdaBoost Classification in Python

January 1, 2019 | 0 Comments

Boosting is a technique in machine learning in which multiple models are developed sequentially. Each new model tries to successful predict what prior models were unable to do. The average for regression and majority vote for classification are used. For classification, boosting is commonly associated with decision trees. However, boosting ...
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