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Towards Data Science 08/08/2020 09:31
A visual approach to understand machine learning. Photo by on. Introduction to machine learning. In the traditional hard-coded approach, we program a computer to perform a certain task. We tell it exactly what to do when it receives a certain input. In mathematical terms, this is like saying that we write the f(x) such that when users feed the input x into f(x) , it gives the correct output y . In machine learning, however, we have a large set of inputs x and corresponding outputs y but not the function f(x) . The goal here is to find the f(x) that transforms the input x into the output y. Well, that’s not an easy job. In this article, we will learn how this happens. Dataset. To visualize the dataset, let’s make our synthetic dataset where each.
Towards Data Science 08/07/2020 22:08
Using Python, Tableau, Machine Learning and TabPy. Photo by. Data science is all about presenting insights to the end-users in the most simplistic way possible. You work on a machine learning/deep learning model from data cleaning to hyperparameter tuning. However, you realize that the most important task of presenting it to the end-users has not even started yet. Here I discuss an easy and faster way to deploy ML models using Jupyter Notebook and Tableau . We will use to process the data and build the model. Then we use to deploy the built model and access it in Tableau. If you are looking for a way to deploy models to use it in cloud platforms or distributed systems, you can discontinue reading now. The Data. We will use the dataset availabl.

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