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Machine Learning for Everybody

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This Machine Learning course provides a comprehensive introduction to the field, starting with an overview of data and Google Colab, a platform used for writing and executing Python in the browser.

The course then delves into the basics of Machine Learning, explaining key concepts such as features, classification, and regression. It guides learners through the process of training a model and preparing data for machine learning tasks.

The course covers several machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, and Support Vector Machine (SVM), each followed by a practical implementation session.

The course then transitions into Neural Networks, introducing TensorFlow, a popular open-source platform for machine learning. It provides hands-on experience in building a Classification Neural Network using TensorFlow.

The course also covers Linear Regression, a fundamental algorithm in machine learning, and demonstrates how to implement it. It further explores how to use a neuron for Linear Regression and how to build a Regression Neural Network using TensorFlow.

Towards the end, the course introduces K-Means Clustering and Principal Component Analysis (PCA), two techniques used for data clustering and dimensionality reduction, respectively. The course concludes with practical implementations of K-Means and PCA.

Throughout the course, learners gain hands-on experience in implementing various machine learning algorithms, providing a solid foundation for further exploration in the field.

Machine Learning course

✏️ Kylie Ying developed this course. Check out her channel:    / ycubed  

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Clara Prieto García is the accomplished Editor of AIResearchNews.com, a leading online news platform dedicated to artificial intelligence research and advancements. With a Master's degree in Information Technology from the Universidad Politécnica de Madrid and over 20 years of experience in journalism, Clara has become a respected figure in the AI community. She has a keen eye for identifying groundbreaking AI developments and is committed to making this knowledge accessible to a global audience. Under her leadership, AIResearchNews.com has garnered a reputation for its comprehensive coverage and insightful analysis of the AI landscape.

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