Statistics & Probability for Machine Learning
Build the mathematical intuition behind ML algorithms. Master descriptive statistics, probability distributions, hypothesis testing, and Bayesian thinking — with Python throughout.
What You'll Learn
Calculate and interpret mean, median, variance, and standard deviation
Understand and apply probability rules, conditional probability, and Bayes' theorem
Identify and work with key probability distributions (Normal, Binomial, Poisson)
Perform hypothesis tests: t-test, chi-square, ANOVA
Compute and interpret correlation and linear regression coefficients
Apply statistical thinking to evaluate ML model performance
Curriculum Breakdown
Learning Format
Video Lessons
High-quality recorded lessons you can watch at your own pace.
Hands-on Projects
Real-world projects that reinforce every concept you learn.
Certificate
Earn a verifiable certificate upon successful completion.
Certification Details
Statistics & Probability for ML Certificate
Issued by Tech101
Demonstrate the statistical foundations that underpin all machine learning algorithms.
Certificate Requirements
- Shareable on LinkedIn
- PDF download
- Unique verification ID
Completion Certificate
Awarded upon finishing all course content and submitting projects. Shows dedication and completion.
Graded Certificate
Earned by passing the final assessment with 70%+ score. Demonstrates verified skill proficiency.
Your Instructor
Sarah Johnson
Data Scientist & ML Engineer
Sarah has 10 years of experience in data science and machine learning, previously at Google AI and DeepMind.
Requirements & Prerequisites
Technical Requirements
- Python basics and NumPy fundamentals
- High-school level algebra
Who This Course Is For
- Data science students who want to understand ML algorithms deeply
- Developers moving into machine learning roles
- Anyone who skipped stats class and now needs it
Student Reviews
Frequently Asked Questions
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