Train Neural Networks in Python: TensorFlow, Scikit-learn and Keras

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Machine learning is a rapidly growing field that allows computers to learn and make predictions or decisions without being explicitly programmed to do so. Python is a popular programming language for machine learning because of its simplicity and the abundance of powerful libraries available. In this article, we will discuss how to use Python for machine learning.

Python offers a variety of libraries for machine learning, such as TensorFlow, Keras, Scikit-learn, and PyTorch. These libraries provide a wide range of machine learning algorithms and make it easy to implement them in your projects.

Let us begin.

Scikit-learn

One of the most popular libraries for machine learning in Python is Scikit-learn. Scikit-learn is a powerful library that provides a wide range of machine learning algorithms, including regression, classification, clustering, and dimensionality reduction.

This is how you can use Scikit-learn to train a machine learning model:

from sklearn import datasets
from sklearn.linear_model import LinearRegression

# Load the diabetes dataset
diabetes = datasets.load_diabetes()

# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]

# Split the data into training/testing sets…