In designing customer facing Machine Learning systems, a key challenge we face is ensuring that our predictions are not only accurate but also interpretable and actionable. It’s possible with open source libraries to gather insights that provide explainability allowing stakeholders to understand why a particular prediction was made that
There has been much recent interest in studying Causal analysis and Causal reasoning in the data science and machine learning research communities. I think this is interesting because much of human knowledge is derived from observational studies. Machine Learning allows us to make Predictions and answer the question: what will
Experts are saying Looks like Python, feels like Lisp, runs like Fortran. That made me curious. So i decided to explore more. Here is a summary of what i found Julia is a general-purpose interpreted language developed by a group at MIT, with numerical computing at the core of its
I recently took the design your life Stanford course. It is an interesting course that applies design thinking to ' transform our present job and our experience of work by utilizing the designer mindsets'. During the course, the part where i struggled or had to spend the most time
SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate relational databases. It allows users to retrieve, update, insert, and delete data efficiently. If you're learning SQL, i recommending focusing on following topics: * SELECT + WHERE + JOIN (most used) * GROUP BY + HAVING (summarizing data)
In this blog post, we begin with the real-world challenge of improving nutrient use efficiency in modern agriculture. We introduce the research gap by showing that although NUE is widely used, its traditional ratio-based definition is statistically flawed. We then highlight the key contribution of this work evaluating alternative statistical
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