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De-Mystifying Math and Stats for Machine Learning
De-Mystifying Math and Stats for Machine Learning

De-Mystifying Math and Stats for Machine Learning: Mastering the Fundamentals of Mathematics and Statistics for Machine Learning

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Profile Icon Seaport AI Madhavan
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₹714.99
eBook Jun 2024 63 pages 1st Edition
eBook
₹714.99
Arrow left icon
Profile Icon Seaport AI Madhavan
Arrow right icon
₹714.99
eBook Jun 2024 63 pages 1st Edition
eBook
₹714.99
eBook
₹714.99

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Key benefits

  • Detailed exploration of key mathematical and statistical concepts for Machine Learning.
  • Logical flow from basic to advanced topics for seamless knowledge building.
  • Engaging materials designed to enhance learning and retention.

Description

Beginning with basic concepts like central tendency, dispersion, and types of distribution, this course will help you build a robust understanding of data analysis. It progresses to more advanced topics, including hypothesis testing, outliers, and the intricacies of dependent versus independent variables, ensuring you grasp the statistical tools necessary for data-driven decision-making. Moving ahead, you'll explore the mathematical frameworks crucial for machine learning algorithms. Learn about the significance of percentiles, the distinction between population and sample, and the vital role of precision versus accuracy in data science. Chapters on linear algebra and regression will enhance your ability to implement and interpret complex models, while practical lessons on measuring algorithm accuracy and understanding key machine learning concepts will round out your expertise. The course culminates with an in-depth look at specific machine learning techniques such as decision trees, k-nearest neighbors (kNN), and gradient descent. Each chapter builds on the last, guiding you through a logical progression of knowledge and skills. By the end, you will have not only mastered the theoretical aspects but also gained practical insights into applying these techniques in real-world scenarios.

Who is this book for?

This course is designed for technical professionals, data analysts, and aspiring data scientists who are keen to deepen their understanding of the mathematical and statistical principles behind machine learning. Ideal for those with a basic grasp of algebra and statistics, this course will elevate your data analysis capabilities and enhance your proficiency in developing and fine-tuning machine learning models. Familiarity with programming concepts is recommended to fully benefit from the course content.

What you will learn

  • Master the fundamentals of central tendency and dispersion.
  • Understand the different types of data distributions.
  • Differentiate between precision and accuracy in data analysis.
  • Conduct hypothesis testing and identify outliers.
  • Apply linear algebra and regression techniques in machine learning.
  • Implement decision trees, kNN, & gradient descent algorithms.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 11, 2024
Length: 63 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207443
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon DRM FREE - Read whenever, wherever and however you want
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OR
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Product Details

Publication date : Jun 11, 2024
Length: 63 pages
Edition : 1st
Language : English
ISBN-13 : 9781836207443
Category :

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Table of Contents

18 Chapters
Table of Contents Chevron down icon Chevron up icon
Chapter 1: Introduction Chevron down icon Chevron up icon
Chapter 2: Central Tendency vs. Dispersion Chevron down icon Chevron up icon
Chapter 3: Types of Distribution Chevron down icon Chevron up icon
Chapter 4: Precision vs. Accuracy Chevron down icon Chevron up icon
Chapter 5: Percentiles Chevron down icon Chevron up icon
Chapter 6: Dependent Vs. Independent Variables Chevron down icon Chevron up icon
Chapter 7: Types of Data Chevron down icon Chevron up icon
Chapter 8: Population Vs. Sample Chevron down icon Chevron up icon
Chapter 9: Hypothesis Testing Chevron down icon Chevron up icon
Chapter 10: Outliers Chevron down icon Chevron up icon
Chapter 11: Machine Learning Concepts Chevron down icon Chevron up icon
Chapter 12: Measuring Accuracy in Algorithms Chevron down icon Chevron up icon
Chapter 13: Maths behind Regression Chevron down icon Chevron up icon
Chapter 14: Linear Algebra in Machine Learning Chevron down icon Chevron up icon
Chapter 15: Decision Tree Chevron down icon Chevron up icon
Chapter 16: k Nearest Neighbours (kNN) Chevron down icon Chevron up icon
Chapter 17: Gradient Descent Chevron down icon Chevron up icon
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