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January 2020 | Volume 2, Issue 1 | Subscribe Spotlight An Epidemic of ML Misinformation ...
Today is an exciting day for me. After months of hard work, IBM, the University of Pennsylvania, and the Linux Foundation are announcing an ...
Introduction Today, we will be using a very fascinating R library which is extensively used for automating algorithms and repeated testing ...
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Neeraj Jangid is a budding data scientist who is currently enrolled in a masters in engineering management program at Southern Methodist University. After being introduced to data science at school, he decided to start blogging and sharing his knowledge and enthusiasm with other prospective data scientists. We spoke with him to learn about his passion for data science and his career ambitions....Read more
Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly. However, it is worth taking the time to look inside and understand underlying concepts.
NumPy is a powerful Python library that can greatly increase the speed and efficiency of processing large data sets. Several data science and machine learning frameworks work in conjunction with or are built on top of NumPy.
As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up — from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering.