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 How to create stunning visualizations using python from scratch
Data science and data analytics can be beautiful things. Not only because of the insights and enhancements to decision-making they can provide, but because of the rich visualizations about the data that can be created. Following this step-by-step guide using the Matplotlib and Seaborn libraries will help you improve the presentation and effective communication of your work.
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One question to make your data project 10x more valuable
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.
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Machine learning is going real-time
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
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Popular Machine Learning Interview Questions, part 2
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.
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 The Ultimate Scikit-Learn Machine Learning Cheatsheet
With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.
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Data Engineering — the Cousin of Data Science, is Troublesome
A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.
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Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
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 Popular Machine Learning Interview Questions
Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.
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Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
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K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
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How to create stunning visualizations using python from scratch
The Ultimate Scikit-Learn Machine Learning Cheatsheet
Popular Machine Learning Interview Questions