STATS-ML.AW1
Statistics For Machine Learning
Reskill, learn, and own machine learning statistics because the future doesn’t wait for the unprepared.
- 12 Interactive Lessons and 141 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
12Interactive Lessons
141Topics
01 / Skills you'll get
What you will be able to do
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No credit card required
Master the statistics for machine learning with this hands-on course.
In this course, dive into essential statistical concepts and apply them in Python for machine learning. Learn how to process data, run tests, and build models using key Python libraries like Pandas, NumPy, and more.
From foundational math to advanced techniques like ANOVA and non-parametric tests, you’ll get step-by-step training.
- Statistical Foundations for ML: Master core statistical concepts like probability distributions, hypothesis testing, and regression analysis, essential for machine learning.
- Data Analysis with Python: Learn to process, explore, and visualize data using Python libraries like Pandas, NumPy, and Matplotlib.
- Hypothesis Testing & Inference: Gain expertise in performing statistical tests (Z-test, T-test, ANOVA) to validate machine learning models.
- Regression & Predictive Modeling: Build and interpret linear, logistic, and advanced regression models for accurate predictions.
- Non-Parametric & Bayesian Statistics: Apply alternative statistical methods like Mann-Whitney, Kruskal-Wallis, and Bayes’ Theorem for real-world data challenges.
- Machine Learning Readiness: Transition smoothly into ML by understanding how statistics powers algorithms like K-NN, SVM, and clustering techniques.
Course Highlights
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12 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 141 topics01 Preface +
02 Introduction to Statistics 6 topics +
- Population and Sample
- Introduction to Random Variables
- Other variables
- Introduction to Descriptive Statistics
- Visualizations
- Conclusion
03 Descriptive Statistics 4 topics +
- Measures of Central Tendency
- Measures of dispersion
- The Strength of the relationship between variables
- Conclusion
04 Random Variables 11 topics +
- Random Variables
- Discrete Random Variables
- Continuous Random Variables
- Joint Distributions
- Independent Random Variables
- Marginal and Conditional Distributions
- Definition of Mathematical Expectation
- Properties of Mathematical Expectation
- Chebyshev’s Inequality
- Law of large numbers
- Conclusion
05 Probability 7 topics +
- Introduction
- Properties of probability
- Some other terminologies
- Conditional probability
- Bayes’s theorem
- Probability distributions
- Conclusion
06 Parameter Estimation 11 topics +
- Parameter estimation
- Point estimate – The mathematics way
- Sampling distributions
- Central Limit Theorem
- Estimators having bias component
- The variance of a point estimate
- Standard Error of Estimator
- Mean Squared Error of Estimator
- Methods to Determine Point Estimates
- Confidence Intervals
- Conclusion
07 Hypothesis Testing 18 topics +
- Hypothesis
- Hypothesis Testing
- Confidence Interval
- Types of Hypothesis
- Null Hypothesis
- Alternative Hypothesis
- P-Value
- Steps in hypothesis testing
- Use Case
- Z-test
- T-test
- One-sample T-test
- Two-sample T-test
- Paired T-test
- Chi-Square test
- Test of Goodnessoffit
- Independence test
- Conclusion
08 Analysis of Variance 15 topics +
- Introduction to ANOVA
- One-way ANOVA test
- Calculation of Mean Square due to Error
- Calculation of Mean Square due to Treatment
- Decision Rule
- Tukey test
- Two-way ANOVA
- Main Effects
- Interaction Effects
- Multivariate Analysis of Variance (MANOVA)
- Wilks’ Lambda test
- Lawley Hotelling Trace
- Pillai’s Trace
- Roy’s Largest Root
- Conclusion
09 Regression 19 topics +
- Simple Linear Regression
- Finding the Values of β0 and β1
- Standard Error
- Confidence Intervals
- Unimportant Variable
- Accuracy of Prediction
- Data Pre-processing
- Multiple Linear Regression
- Polynomial Regression
- Subset Selection Method
- Ridge Regression
- Lasso Regression
- ElasticNet Regression
- Logistic Regression
- Estimation of Parameters
- Understanding Residuals
- Patterns of Residuals
- Multicollinearity
- Conclusion
10 Data Analysis Using Python 29 topics +
- Pandas
- Importing and Reading a CSV Sheet
- Basic Exploration of Data
- Converting a Python Data Structure to Data Frame
- Numerical Description of a Data Frame
- Adding Conditions in Pandas
- Extending Extractions – loc and iloc
- Understanding the iloc() Function
- Understanding the loc() Function
- Tackling Null Values
- Concatenating Data Frames
- Merging Data Frames
- Left Join
- Right Join
- Outer Join
- Inner Join
- Reading and Writing Excel Sheets
- Exploring Groupby
- Binning in Pandas
- Pandas Series
- NumPy
- Creating Null Vector
- Indexing
- Reshaping a Numpy Array
- Generating Random Values Using Numpy
- Descriptive statistics using Numpy
- Mathematical Operations Using Numpy
- Other important features in Numpy
- Conclusion
11 Non-Parametric Statistics 8 topics +
- The test for randomness
- Sign Tests
- One-sample Sign Test
- Wilcoxon Test
- Mann Whitney Test
- Spearman Rank Correlation Test
- Kruskal Wallis test
- Conclusion
12 Introduction to Machine Learning 13 topics +
- Machine Learning
- Supervised Learning
- K-Nearest Neighbour
- Naive Bayes Theorem
- Decision trees
- Ensemble trees
- Support Vector Machines
- Python application
- Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Conclusion
03 / FAQs
Questions before you start
Do I need prior knowledge of statistics or ML?+
No, this Statistics for Machine Learning course starts with the fundamentals, making it perfect for beginners. However, basic Python knowledge will help you apply statistical concepts in coding exercises.
Will I learn probability and distributions?+
Yes! You’ll dive deep into probability theory, key distributions (Normal, Binomial, Poisson, etc.), and how they apply to machine learning.
How is this course different from general statistics courses?+
Unlike traditional stats courses, this one focuses on real-world ML applications, teaching you how to use statistics for model evaluation, hypothesis testing, and data preprocessing.
Will this help me in machine learning interviews?+
Definitely. Many ML interviews test statistical concepts covered here. Probability, hypothesis testing, regression, and data analysis make this course great for interview prep.
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