Data Science & Machine Learning

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You should definitely check the documentation of numpy and scikit learn if you are facing difficulty in answering above questions https://numpy.org/doc/stable/ https://scikit-learn.org/ Share the channel link with your friends to help them too in learning data science and machine learning 👇👇 http://t.me/datasciencefun

Just a correction here "np.nan" instead of "na.nan" Hope you all are aware that mostly we import numpy as np

Majority is correct Well done guys 👍👍 Scikit learn can be specially used to solve classification, regression, clustering and dimensionality reduction problems

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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science. Basically, there are 3 different layers in a neural network : Input Layer (All the inputs are fed in the model through this layer) Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers) Output Layer (The data after processing is made available at the output layer) Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.