Learners will be able to apply probability, sampling, distributions, and statistical testing to analyze datasets and build machine learning models with Python. By the end of this course, they will differentiate data types, evaluate hypothesis testing approaches, and utilize linear algebra and inferential methods to interpret and validate results in real-world contexts.



Machine Learning with Python & Statistics
This course is part of AI Machine Learning with R & Python Projects Specialization

Instructor: EDUCBA
Access provided by Woxsen University
What you'll learn
Apply probability, sampling, and distributions to datasets.
Use linear algebra and hypothesis testing for data analysis.
Build and validate ML models with Python in real-world contexts.
Skills you'll gain
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14 assignments
October 2025
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There are 4 modules in this course
This module introduces learners to the essential foundations of Machine Learning with Python, exploring its core concepts, real-world applications, and the critical role of data mining in uncovering patterns. Students will gain a strong conceptual base to understand how machine learning systems differ from traditional programming and how data-driven insights power intelligent decision-making.
What's included
8 videos3 assignments1 plugin
This module introduces learners to the essential concepts of sampling methods and statistical data types in Machine Learning. It explores systematic, cluster, and stratified sampling techniques, while also distinguishing between qualitative, quantitative, discrete, continuous, nominal, and ordinal data. By mastering these foundations, learners will understand how data collection and classification impact the accuracy, reliability, and effectiveness of machine learning models.
What's included
8 videos3 assignments
This module provides a comprehensive foundation in probability theory, random variables, and linear algebra concepts essential for machine learning. Learners will explore probability fundamentals such as conditional probability, independence, and the law of total probability, then advance into discrete and continuous distributions including Bernoulli, geometric, and normal distributions. The module also introduces linear algebra essentials—matrices, transposes, and determinants—equipping learners with mathematical tools required to build and analyze machine learning models effectively.
What's included
16 videos4 assignments
This module equips learners with the statistical foundations required to test hypotheses, interpret confidence intervals, and apply advanced inferential techniques in machine learning. Learners will explore error types, critical value and p-value approaches, tail tests, and confidence intervals. The module then advances into applied inferential statistics with t-tests, Chi-square tests, and goodness of fit measures, as well as the interpretation of covariance. By the end, learners will be able to conduct robust statistical testing and evaluate data relationships with accuracy.
What's included
23 videos4 assignments
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