First Import numpy and define a class named "PCA" to encapsulate the operations. In the __init__ constructor let's initialize the number of components to reduce to, which is n_components, and similarly create placeholders for mean and eigenvectors.
@sumanth_077
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Principal Component Analysis PCA Explained and Implemented in Python
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Princiapal Component Analysis (PCA) clearly explained and implemented from scratch in Python:
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3. Machine Learning for Beginners This covers and helps you learn: – Various ML Techniques
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microsoft.github.io/ML-For-Beginne
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Data Science for Beginners: Complete Learning Guide and Resources
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2. Data Science for Beginners This covers and helps you learn: – Data Science Basics & Ethics
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microsoft.github.io/Data-Science-F
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microsoft.github.io/AI-For-Beginne
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Microsoft Free Data Science and Machine Learning Courses
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Microsoft is offering FREE courses to jumpstart your career in Data Science and Machine Learning. Here are 3 free courses you don't want to miss:
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Daily Content on Python Data Science Machine Learning and MLOps
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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5. Data Science IPython There are many Libraries in Machine Learning so having a Jupyter Notebook Explanations to all those is really handy. This Repository will help you with that.
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3. Awesome Machine Learning A repository with a curated list of awesome machine learning frameworks, libraries and software: