Mathematics & Statistics for Machine Learning
Self-paced video training — 14 modules, 14 lessons. Work through it at your own pace — every lesson is real recorded training, not slide-reading.
▶INSIDE THE COURSE · 14 LESSONSABOUT THIS COURSE
In this intermediate course, students will explore key mathematical and statistical concepts essential for machine learning. Topics include understanding central tendency and dispersion, calculating percentiles, and distinguishing between dependent and independent variables. By the end of the course, learners will be equipped to apply these concepts to analyze data effectively and support machine learning algorithms.
CURRICULUM
14 modules, in order.
01Introduction1 LESSONS
this lesson provides an overview of the course content and objectives.
02Central Tendency Vs Dispersion1 LESSONS
this lesson explains the concepts of central tendency and dispersion in data.
03Percentiles1 LESSONS
this lesson covers the concept of percentiles in data analysis.
04Dependent Vs Independent Variable1 LESSONS
this lesson differentiates between dependent and independent variables.
05Types of Data1 LESSONS
this lesson concludes the types of data discussed throughout the course.
06Sampling1 LESSONS
this lesson introduces the concept of sampling in statistics.
07Hypothesis Testing1 LESSONS
this lesson covers the principles of hypothesis testing.
08Oultiers1 LESSONS
this lesson discusses the importance of identifying outliers in data.
CURATED BY
Selected from experienced educators' training libraries and kept whole — modules, worked examples and walkthroughs exactly as taught.
GOOD TO KNOW
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Watch on two devices at once — phone, tablet or desktop.
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$39 · ONE PAYMENT
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One payment unlocks all 14 lessons instantly — start with lesson one the moment you check out.