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We're here to help you find itEssential Maths & Statistics for Machine Learning Course Overview
The Essential Maths & Statistics for Machine Learning course is designed to provide a foundation in the mathematical and statistical concepts that underpin machine learning algorithms and models. Learners will gain insights into why mathematics is fundamental for creating effective machine learning applications, delve into statistics for data analysis and model evaluation, and understand basic algebra for handling equations and functions.
This mathematics for machine learning course covers essential topics such as calculus for optimization, linear algebra for data transformation, probability for making predictions under uncertainty, descriptive statistics for data summarization, and inferential statistics for making data-driven decisions. Through various modules, the course equips participants with the tools needed for model selection, interpretation, and the creation of compelling data visualizations. It's designed to help learners build a strong mathematical foundation, enabling them to implement and innovate with machine learning algorithms effectively.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
USD 199+
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USD 59+
♱ Excluding VAT/GST
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To ensure that learners are well-prepared and can fully benefit from the Essential Maths & Statistics for Machine Learning course, the following prerequisites are recommended:
It is important to note that the course is designed to build upon these foundational skills, so even if learners are not experts in these areas, a basic understanding will enable them to follow the course material and develop their skills further.
The Essential Maths & Statistics for Machine Learning course is designed for individuals aiming to master foundational skills for ML applications.
Gain foundational knowledge in mathematics and statistics essential for developing and understanding machine learning algorithms, including algebra, calculus, linear algebra, probability, data visualization, and inferential statistics.