Essential Maths & Statistics for Machine Learning Course Overview

Course Prerequisites

Target Audience for Essential Maths & Statistics for Machine Learning

The Essential Maths & Statistics for Machine Learning course is designed for individuals aiming to master foundational skills for ML applications.

• Data Scientists and Machine Learning Engineers
• Statisticians transitioning to predictive analytics
• Software Developers interested in artificial intelligence and machine learning
• Students pursuing degrees in computer science, data science, or related fields
• Data Analysts looking to upgrade their skills to include machine learning
• AI Enthusiasts seeking to understand the mathematical underpinnings
• Researchers requiring a solid foundation in maths and statistics for ML
• IT Professionals aspiring to switch to a data-centric role
• Business Analysts wanting to apply ML models to interpret complex data
• Quantitative Analysts in finance or other sectors integrating ML into their analysis
• Product Managers overseeing data-driven projects or products
• Academic professionals and educators teaching ML concepts
• Technical Leads and Managers needing a grasp of ML fundamentals for team leadership

Learning Objectives - What you will Learn in this Essential Maths & Statistics for Machine Learning?

Introduction to Course Learning Outcomes:

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.

Learning Objectives and Outcomes:

• Understand the fundamental role of mathematics in the formulation and optimization of machine learning algorithms.
• Apply algebraic and quadratic equations to solve problems related to machine learning models.
• Utilize calculus concepts such as differentiation and integration for model optimization, specifically in techniques like gradient descent.
• Comprehend and perform vector and matrix operations critical for data manipulation and transformation in linear algebra.
• Grasp the basics of probability and employ conditional probability in modeling random processes and variables.
• Analyze data using descriptive statistics, measure central tendency, and dispersion to understand data distribution.
• Create meaningful data visualizations to interpret and communicate data insights effectively using tools like Matplotlib.
• Differentiate between population and sample, and understand the importance of sample bias and sampling distributions.
• Assess relationships between variables using concepts of correlation, causality, and covariance.
• Conduct hypothesis testing to draw inferences from data, understanding statistical significance and confidence intervals.