What you'll learn
Class Introduction
What exactly is machine learning
Choosing a Model Based on Your Data Underfitting
Choosing a Model Based on Your Data Overfitting
Shallow Machine Learning vs Deep Machine Learning
Installing the Surprise Open-Souce Library
How to Evaluate Your Machine Learning Algorithm
Building Your Machine Learning Skelaton
Theory Behind Singular Value Decomposition (SVD)
Theory Behind K Nearest Neighbor (KNN) Algorithim
Class Conclusion
Requirements
Basic of Machine Learning in Python
Basic Computer Knowledge
Description
Applying the Basics of Machine Learning in Python.
This class can serve as you complete beginner's guide to getting your feet wet in the world of machine learning. Machine learning is certainly the future, but it can definitely be intimidating to get started with. However, not to worry! With this starter-level class, we will go over different aspects such as what machine learning actually is, some of the theory behind it, and finally, we will be creating a project in Python to practice our newly acquired knowledge, where we will try some different algorithms in an open source framework.
This book teaches you how to apply the basics of machine learning in Python. You will learn how to build and evaluate predictive models with the help of real-world data, how to perform various types of regression analysis, and how to identify interesting patterns in your data.
Applying the Basics of Machine Learning in Python is a guide to using machine learning in Python, using easy-to-understand examples and clear instructions. The book covers the different types of machine learning, how to train a model, how to test a model, and more. The author provides a number of code examples that demonstrate how to use different machine learning algorithms in Python.
Who this course is for
Beginners Basics of Machine Learning in Python and improve your Knowledge
Homepage

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