Differences Between Tensorflow and Scikit-Learn
For machine learning beginners,sometimes its hard to choose the best library for machine learning.This article clarifies the differences and gives an idea on which library to use in different problems.
A brief Overview….

TensorFlow is more of a low-level library.basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas scikit-learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression, and many, many more. TensorFlow really shines if we want to implement deep learning algorithms, since it allows us to take advantage of GPUs for more efficient training.
Get it?
TensorFlow is a low-level library that allows you to build machine learning models (and other computations) using a set of simple operators, like “add”, “matmul”, “concat”, etc. scikit-learn is a higher-level library that includes implementations of several machine learning algorithms, so you can define a model object in a single line or a few lines of code, then use it to fit a set of points or predict a value.
Tensorflow is mainly used for deep learning while scikit learn is used for machine learning.
