Beginners Guide to Machine Learning - Python, Keras, SKLearn

Beginners Guide to Machine Learning - Python, Keras, SKLearn

Beginners Guide to Machine Learning - Python, Keras, SKLearn The foundations of machine learning, taught in an engaging and concise way

Category :  Machine Learning

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"A Beginner's Guide to Machine Learning with Python, Keras, and scikit-learn" is likely a guide or tutorial for people new to the field of machine learning, who want to learn how to use the Python programming language, along with the Keras and scikit-learn libraries, to build and train machine learning models.

Python is a popular programming language for machine learning because it has a large number of libraries and frameworks that make it easy to implement machine learning algorithms. Keras is a high-level neural network API, written in Python and capable of running on top of TensorFlow (or Theano/CNTK). It is a user-friendly and intuitive framework for building and training neural networks. scikit-learn, also known as sklearn, is an open-source library for machine learning in Python, which provides easy-to-use interfaces for tasks such as classification, regression, and clustering.

The guide would likely cover the basic concepts of machine learning, as well as walk the reader through the process of building and training different types of machine learning models using Python, Keras, and scikit-learn. It might include practical examples and exercises to help readers solidify their understanding of the material.

What you'll learn

• Gain a foundational understanding of machine learning
• Implement both supervised and unsupervised machine learning models
• Measure the performances of different machine learning models using the suitable metrics
• Understand which machine learning model to use in which situation
• Reduce data of higher dimensions to data of lower dimensions using principal component analysis