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Learn python libraries: Build machine learning model in 2021

Learn about python libraries that are used to solve machine learning problems
Instructor:
Sufa Digital Media
284 students enrolled
English [Auto]
Numpy
Pandas
Matplotlib
Sklearn

Python has been the go-to choice for Machine Learning and Artificial Intelligence developers for a long time. Python offers some of the best flexibilities and features to developers that not only increase their productivity but the quality of the code as well, not to mention the extensive libraries helping ease the workload. Various features that put Python among the top programming languages for Machine Learning, Deep Learning and Artificial Intelligence are listed below:

Free and open-source nature makes it community friendly and guarantees improvements in the long run

Exhaustive libraries ensure there’s a solution for every existing problem

Smooth implementation and integration make it accessible for people with the varying skill level to adapt it

Increased productivity by reducing the time to code and debug

Can be used for Soft Computing, Natural Language Processing as well

Works seamlessly with C and C++ code modules

Scikit-learn is another actively used machine learning library for Python. It includes easy integration with different ML programming libraries like NumPy and Pandas. Scikit-learn comes with the support of various algorithms such as:

Classification

Regression

Clustering

Dimensionality Reduction

Model Selection

Preprocessing

Built around the idea of being easy to use but still be flexible, Scikit-learn is focussed on data modelling and not on other tasks such as loading, handling, manipulation and visualization of data. It is considered sufficient enough to be used as an end-to-end ML, from the research phase to the deployment.

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