Succeed
    Introduction to Machine Learning
    Online Program

    Introduction to Machine Learning

    by Stanford Pre-Collegiate Studies

    Study machine learning through Python, from preparing and visualizing data to testing models and presenting a final selection.

    Last verified: 6 Oct 2026Reviewed by:SSSean Stevens

    Get the full picture before you decide

    See Succeed's assessment of the programme, who it may suit, the estimated total cost, what applying involves and what is still to confirm.

    or let us know if .

    • What Succeed checks

      We review public online program details such as eligibility, age range, cost, duration, learning format, scheduling, mentor model, outputs and application timing.

    • What Last verified means

      Last verified is the latest date Succeed checked the public details shown on this page. Program dates, prices and availability can still change.

    • How official sources are used

      Succeed uses official provider information where available, then rewrites it into a student-facing summary for comparison and planning.

    • Why some dates are reference dates

      If the current cycle is not available, Succeed may show the latest verified cycle dates so you can understand typical timing.

    • Succeed is independent from this provider unless a partnership is clearly stated.

    Introduction to Machine Learning at a glance: cost, dates and eligibility

    Current status

    Upcoming

    Eligibility

    Grades 10-11; programming and statistics prerequisites

    Age range / Year group

    Ages 15-17

    Location / Region

    Online

    Cost

    $3,200

    Duration

    2 weeks

    Format

    Online

    Next deadline

    Not announced · Next cycle not announced

    Get next-cycle reminder
    Computer ScienceEngineeringMathematics
    Sessions / cadence: 10×2h Mon-Fri; 1-2h/day homework
    Assessment style: Assignments, projects, and model presentation
    Main deliverable / output: Final model selected for presentation
    Time-zone / scheduling model: 4:00-7:00 p.m. PDT window
    Latest verified cycle: 2026
    Academic support: Online office hours

    Introduction to Machine Learning summary

    Stanford Pre-Collegiate Summer Institutes teaches a practical machine learning workflow using Python, connecting data preparation with model selection.

    Students preprocess and visualize data, train and tune models, and evaluate performance before selecting a model for presentation.

    The strongest fit is a student who wants structured technical practice and can sustain live classes alongside independent assignments.

    What is Introduction to Machine Learning?

    Introduction to Machine Learning is a Stanford Pre-Collegiate Summer Institutes course exploring how algorithms extract insights from large datasets. Its academic focus connects computer science, engineering, and mathematics through practical work in Python.

    Students follow the machine learning process from preparing structured data to training, testing, and tuning models. Live online classes, independent assignments, and online office hours support that work, culminating in selecting a final model for presentation.

    Why Succeed highlights Introduction to Machine Learning

    • The workflow connects data preparation, model training, and evaluation rather than treating coding as an isolated exercise.

    • Python-based assignments give students a practical way to apply machine learning concepts.

    • Daily live teaching and online office hours provide structure around independent project work.

    • Selecting a final model for presentation encourages students to explain technical choices.

    Who is Introduction to Machine Learning for?

    Best for

    Students who enjoy programming and statistics and want to apply both to a complete machine learning workflow.

    Not ideal if

    You want a first introduction to programming or need a flexible, entirely self-paced course.

    Is Introduction to Machine Learning worth it?

    This course is worth considering if you want guided practice connecting programming, statistics, and model evaluation. Its value lies in understanding the workflow and explaining your decisions, especially when you engage fully with assignments and office hours.

    The concentrated schedule requires sustained attention alongside independent work, so it suits students ready for technical study. Treat it as academic enrichment: Stanford explicitly separates participation from its undergraduate admissions process.

    Sean, founder of Succeed

    Sean

    Founder, Succeed

    Talk it through with Sean

    Want to talk it through?

    “I can help you compare the programmes you're considering, think through the practicalities and work out what makes sense for you.”

    20 minutes · No obligation to enrol

    A practical overview of cost, time commitment, provider details, student outcomes and online support to review before deciding.

    Cost and what is included

    $3,200; includes Live online classes., Online office hours., Assignments and projects..

    Dates and time commitment

    2 weeks; 10×2h Mon-Fri; 1-2h/day homework.

    Provider details

    Review the Stanford Pre-Collegiate Studies provider page and official sources before making a final decision.

    What the student gains

    Potential output: The main output is a final machine learning model selected for presentation.. Check whether feedback, certificate, recommendation or application evidence is included.

    Online support and safeguarding

    Check how online sessions are supervised, how mentor communication works, and what support route exists if the student needs help.

    What to check before committing

    Confirm current dates, session schedule, payment terms, cancellation rules, support expectations and what output or certificate is included with the provider.

    Compare this program with similar options before deciding on dates, cost, format and fit.

    Compare with similar programs

    How much preparation does Introduction to Machine Learning need?

    Effort level

    High for a short enrichment course.

    Best started

    Allow several weeks before a future application deadline for transcripts, responses, and work-sample preparation.

    Main challenge

    Combining programming and statistics while keeping pace with daily work.

    Typical preparation

    • Review programming fundamentals and statistics before the course.

    • Confirm that you can access Anaconda Distribution.

    • Gather recent grade reports and transcripts, then select a work sample meeting the course's application requirements.

    • Convert the PDT meeting window to local time and reserve time for independent assignments.

    Useful if

    You want to test your interest in practical machine learning.

    What do students do on Introduction to Machine Learning?

    You use Python to move through a practical machine learning workflow, supported by live classes and independent assignments. The work leads toward evaluating competing models and choosing one for presentation.

    Step-by-step process

    1. 1

      Preprocess structured data so it is ready for analysis.

    2. 2

      Visualize data to investigate patterns.

    3. 3

      Train, test, and tune machine learning models.

    4. 4

      Evaluate algorithm performance.

    5. 5

      Select a final model for presentation.

    Introduction to Machine Learning dates and deadlines

    Next cycle not announced yet. These dates are from the latest verified cycle and should be used as a reference only.

    Application deadline

    13 March 2026

    2026 Summer Institutes application; materials due by 11:59 p.m. Pacific Time.

    Reference date

    Session One

    15-26 June 2026

    Weekday live classes within 4:00-7:00 p.m. PDT.

    Reference date

    Session Two

    6-17 July 2026

    Weekday live classes within 4:00-7:00 p.m. PDT.

    Reference date

    Eligibility and requirements

    • Current Grades 10-11 at the time of application.

    • Exposure to a computer programming language.

    • Working knowledge of statistics.

    • Domestic and international applicants may apply.

    • Admitted students may attend only one Summer Institutes course.

    How much does Introduction to Machine Learning cost?

    Program fee

    $3,200 — Two-week Summer Institutes tuition.

    Funding or discounts

    • Financial aid: Domestic and international participants — Both groups may receive financial aid.

    What's included

    Live online classes., Online office hours., Assignments and projects.

    How to apply to Introduction to Machine Learning

    1. 1

      Create an account in the official application portal using the student's legal name and birthdate.

    2. 2

      Select Stanford Pre-Collegiate Summer Institutes and indicate the courses you want to attend.

    3. 3

      Write your own application responses and prepare the required course-specific work sample.

    4. 4

      Upload recent grade reports, transcripts, and all other application materials.

    5. 5

      Review the completed application with a parent or legal guardian for their signature.

    6. 6

      Pay the application fee and submit the complete application; changes are not permitted afterward.

    Next step with Succeed

    Interested in this programme?

    or let us know if .

    Succeed replies first, usually within a working day

    Learning format

    Each session has ten two-hour live online classes, meeting Monday through Friday within a 4:00-7:00 p.m. PDT window, with the third hour used for online office hours. Students should also plan for approximately 1-2 hours of assignments and projects per day outside live class.

    Exact class and office-hour schedules are set closer to the start of the program.

    Mentorship and feedback

    • Two hours of live online teaching each weekday.

    • Online office hours use the third hour of the meeting window.

    • Exact class and office-hour schedules are set closer to the start.

    Time-zone considerations

    • The published meeting window is 4:00-7:00 p.m. PDT.
    • Live classes meet for two hours each Monday through Friday.
    • The remaining hour is used for online office hours.
    • Allow approximately 1-2 additional hours daily for independent work.
    • Students attend one assigned course section and time.

    Tech requirements

    • Students must be able to access Anaconda Distribution.

    Assessment style

    The course uses assignments and projects leading toward final model selection for presentation.

    • Complete assignments and projects outside live class.
    • Evaluate machine learning algorithm performance.
    • Select a final model for presentation.

    Portfolio / outputs

    Main deliverable / output

    The main output is a final machine learning model selected for presentation.

    • Python work preprocessing and visualizing structured data.

    • Course assignments and machine learning projects.

    Introduction to Machine Learning FAQs

    Students need exposure to a computer programming language and working knowledge of statistics. The course uses Python.

    Students should plan for approximately 1-2 hours of assignments and projects per day outside live classes.

    Classes meet for two hours each Monday through Friday within a 4:00-7:00 p.m. PDT window. The third hour is used for online office hours, and exact schedules are set closer to the program.

    Students must be able to access Anaconda Distribution to complete assignments.

    Applicants must write their own responses. Parents or legal guardians may review the completed application and sign it.

    Standardized test scores are optional for domestic and international applicants. Applicants can upload an unofficial transcript to satisfy the transcript requirement.

    Yes. Financial aid can be awarded to both domestic and international participants.

    No. Stanford University's undergraduate admissions process is separate and independent from Stanford Pre-Collegiate Studies programs.

    Alternatives to Introduction to Machine Learning

    Compare these adjacent options if you want broader software development, engineering, or mathematical preparation.

    Alternatives to Introduction to Machine Learning

    Save this program in Succeed to compare it with similar programs, dates, costs and formats in one place.

    Compare with other programs
    Sean Stevens

    Content reviewed by

    Sean Stevens

    Co-founder, Succeed | Founder, Immerse Education (2012–2026)

    Sean works at the intersection of academic enrichment, program quality and university preparation, with expertise in evaluating pre-university experiences for ambitious secondary school students.

    View author page

    Sources & verification

    Source types reviewed

    • SU26_SPCS_Flyer_TeachCoun.indd
    • Welcome
    • Questions: Application - SI | Stanford Pre-Collegiate Summer Institutes
    • Introduction to Machine Learning | Stanford Pre-Collegiate Summer Institutes

    Succeed uses official provider information where available, but keeps this public page focused on comparison and planning inside Succeed.

    What we verified (on 6 Oct 2026)

    • Course requirements checked

    • Session dates checked

    • Live schedule reviewed

    • Homework expectations reviewed

    • Tuition and aid reviewed

    • Application process checked

    How Succeed uses this information

    We use source material to verify core facts, then show older cycle dates as reference when a current cycle is not available. Always check current application instructions before applying.