Menu
CoddyTech
START LEARNING
flag En iconEnglishdown icon
machine Learning iconPart of the Machine Learning JourneyNewJourney

Statistics for Machine Learning

The statistics every model leans on, learned by simulating and plotting instead of memorizing formulas. Real Python with NumPy, pandas, SciPy and scikit-learn, and a chart in every lesson.

START LEARNING(it's free)
View syllabusStatistics for ML playground

1,000+ codders enrolled

64 lessons63 challenges265 quiz questions

  • Beginner friendly
  • sparkles iconAI-assisted coding help
  • hint iconHands-on interactive lessons
  • quiz iconQuizzes to test your knowledge
  • certificate iconFree certificate of completion

Syllabus

1 sections64 lessons63 challenges265 quiz questions

This section is part of the Machine Learning Journey. The full syllabus has more sections - click any preview below to view it on the Journey page.

  1. Section 1Data and Plots60 lessons
  2. Section 2

    Statistics for ML

    Start sectionStartExpandCollapse    The statistics behind every model: center and spread, distributions, probability by simulation, samples, outliers, correlation, trend lines and group tests, then scaling and encoding features. Every idea is computed on real data and plotted.64 lessons63 challenges265 questions

    Center and Spread

    6 lessons625

    Distributions

    6 lessons625

    Probability by Simulation

    6 lessons625

    Samples and Populations

    5 lessons520

    Outliers

    5 lessons520

    Relationships

    6 lessons525

    Trend Lines

    6 lessons625

    Comparing Groups

    6 lessons625

    Feature Scaling

    5 lessons520

    Encoding Categories

    4 lessons415

    Exploring a Dataset

    5 lessons520

    Final Challenges

    4 lessons420
  3. Section 3Supervised Learning58 lessons
  4. Section 4Unsupervised Learning51 lessons
View the full Machine Learning Journey syllabus
What you get
Everything you'll use to learn to code

Learn by Doing

Write real code, query databases, build websites, and master AI prompts. Our interactive lessons cover every skill modern developers need.

playground.js
Code Editor
1const greeting = "Hello, Coddy!"
2function sayHi(name) {
3    return greeting + " " + name
4}
5
bottombar Collapse icon
Test #1test Case Success icon
Test #2test Case Success icon
Test #3test Case Failure icon
Input
"Alex"
Output
"Hello, Coddy! Alex"

Build Your Streak

Stay consistent and watch your progress grow! Track your daily coding habit, protect your streak with freeze days, and earn rewards for showing up every day.

12 days streak

Return tomorrow to keep your streak!

fire Filled icon
left icon

January 2026

right icon

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

21

22

23

24

25

26

27

28

29

30

product Double Or Nothing icon

Double or Nothing

Day 5 of 7

fire Freeze icon

Streak Freeze

2 left

Code Anywhere

Take your coding journey on the go! No setup, no downloads - just open and start coding. Available on iOS, Android and Web with 4.9 star ratings.

Python
7Streak
250Score
5Energy
Variables
journey Hex Done Base iconjourney Hex Done Shadow iconjourney Hex Done Top iconjourney Lesson Done icon
journey Path Right Done icon
journey Hex Done Base iconjourney Hex Done Shadow iconjourney Hex Done Top iconjourney Lesson Done icon
journey Path Left Done icon
journey Hex Active Base iconjourney Hex Active Shadow iconjourney Hex Active Top iconjourney Lesson Theory Challenge icon
CONTINUE
journey Path Right icon
journey Hex Locked Base iconjourney Hex Locked Shadow iconjourney Hex Locked Top iconjourney Lesson Theory Challenge icon
journey Path Left icon
journey Hex Locked Base iconjourney Hex Locked Shadow iconjourney Hex Locked Top iconjourney Lesson All icon
Journey
Goals
Leaderboard
Profile
4.9
StarStarStarStarStar
Rating

You're Not Alone

Compete on global leaderboards, invite friends to earn rewards, and celebrate each other's wins. Coding is better with friends!

Challenger League
Challenger LeagueTop 7 advance
leaderboard First icon1
avatar 1 icon
fire Filled icon
Alex7+ Days
2840
leaderboard Second icon2
avatar 2 icon
fire Filled icon
Jordan7+ Days
2650
leaderboard Third icon3
avatar 3 icon
fire Filled icon
Sam7+ Days
2420
4
avatar 4 icon
Casey
2180
5
avatar placeholder icon
fire Filled icon
Morgan7+ Days
1950
leaderboard Arrow Up iconPromotion zoneleaderboard Arrow Up icon

Every way to learn

Read, listen, test yourself, ask the AI, or look up anything you've already covered. Every lesson meets you where you are.

Intro to Variables
Audio

A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

1xSarah

Earn a Certificate

Earn certificates for every course you complete. Add them to your LinkedIn profile and resume to showcase your coding expertise to employers.

CoddyCertificate of Completion
This certifies thatJohn Doehas successfully completed
python iconPython Fundamentals
Verified
DateJan 2026
LinkedInAdd to LinkedIn

Why learn statistics for ML with Coddy

  • Simulate it, then see it. Roll thousands of dice, draw thousands of samples, shuffle groups thousands of times, and watch the histogram settle. A probability is a share you can count before it is a formula.
  • Every lesson has a chart: density histograms with a normal curve on top, box plots with their outliers, correlation heatmaps, fitted trend lines. Your own chart appears in the Plots tab as you run.
  • The statistics machine learning actually uses: mean and median, spread and percentiles, distributions and z-scores, sampling, outliers, correlation, least squares and R squared, p-values and the t-test.
  • It ends where models begin: feature scaling with StandardScaler, encoding categories with pd.get_dummies, and a checklist for exploring a new dataset before you train anything.

Frequently asked questions about statistics for machine learning

How much statistics do you need for machine learning?

Less than most people fear. To train and judge models well you need to read center and spread, know what a distribution and an outlier are, measure a relationship with correlation, fit a line, and tell a real difference from chance. That is this section. Proofs and heavy probability theory matter for research, not for using scikit-learn well.

Is this the same statistics as for data science?

Yes. Descriptive statistics, distributions, sampling, correlation, regression lines and hypothesis tests are the shared core of statistics for data science and for machine learning. The last chapters lean toward models, with scaling, encoding and exploring a dataset before training.

Do I need calculus or linear algebra?

No. Everything is taught with arithmetic, simulations and charts. Where a formula exists, you meet it as one line of code and a picture, then check it against a simulation. Calculus and linear algebra become useful later if you go deep into how algorithms are built.

What is a p-value, in plain words?

It is how often pure chance would give a result at least as big as yours. In the course you compute it by shuffling: mix two groups together, split them at random thousands of times, and count how often the fake difference beats the real one. Then you meet stats.ttest_ind, the formula shortcut for the same question.

Which Python libraries does it use?

NumPy for the numbers, pandas for tables, matplotlib for every chart, SciPy's stats module for distributions and tests, and scikit-learn for StandardScaler. All of them are taught in the Python data visualization section that comes before this one.

How long does it take?

64 lessons, about 11 hours at your own pace: three to four weeks at half an hour a day, including four final challenges on real-looking data.

Is the statistics section free?

Yes. The lessons, the charts, the AI hints and the section certificate are free. A free account works through a few lessons a day, and Pro removes the daily limit.

Learning paths that include this course

This course is one step of a longer path: several courses in the order a mentor would teach them, each with its own free certificate.

Coddy programming languages illustration

Learn Statistics for ML with Coddy

GET STARTED