Statistics for Machine Learning

Course Overview

By the end of this course, you will be able to apply statistical concepts in Machine Learning, analyze and interpret datasets, perform hypothesis testing, build regression and classification models, evaluate machine learning models using statistical metrics, and make data-driven decisions with confidence. The course includes 20+ hands-on assignments, Python coding exercises, probability worksheets, hypothesis testing problems, regression and classification case studies, model evaluation exercises, mini projects, and a final capstone project using real-world datasets. To earn the Certificate of Completion, learners must complete all lessons, submit assignments, pass quizzes, finish the capstone project, and score at least 60% in the final assessment. This course stands out through its practical, project-based approach, featuring 40 structured lessons, Python implementation for every statistical concept, real-world datasets, industry case studies, coding demonstrations, portfolio-ready projects, interview preparation, and continuous hands-on practice. It is designed to bridge the gap between statistical theory and real-world Machine Learning applications, making students job-ready for careers in Data Science and Artificial Intelligence.

This course is designed for guided live learning. You will move through concepts, Tutor-led demonstrations, practice tasks, review sessions, assignments and a final project that proves your understanding.

Learning Outcomes

Understand descriptive and inferential statistics
Calculate measures of central tendency and dispersion
Work with probability and probability distributions
Apply sampling techniques and Central Limit Theorem
Perform hypothesis testing
Understand confidence intervals and p-values
Build linear regression models
Evaluate classification models
Understand confusion matrix and ROC curve
Apply statistical concepts in Machine Learning
Analyze real-world datasets using Python
Prepare data for predictive modeling

Requirements

Basic computer knowledge
No prior statistics knowledge required
Basic Python knowledge is helpful but optional
Laptop/Desktop with internet connection
Willingness to practice coding

Who This Course Is For

Students
Data Science beginners
Machine Learning enthusiasts
Software Engineers
Data Analysts
Business Analysts
Researchers
Working Professionals
Anyone preparing for AI careers

Assignments

Assignments help the Tutor check your progress and give completion feedback. After purchase, assigned work also appears inside the student dashboard.

1. Introduction to Statistics assignment