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HomeCoursesAi & Machine LearningData Science: Machine Learning

Data Science: Machine Learning

By edX with Harvard University

AI & Machine Learning

6.0
•1 verified reviewTrustScore
Our ratings use a 10-star system for more precise quality assessment
Online Course
24 hours
Beginner
Certificate Included
Visit Provider
Write a ReviewWrite a Story
TrustCourse Rating
Verified
C
TRUST·SCORE
6.0
OUT OF 10
1 verified review
Average
Rating Distribution% of reviews
9-10
0%
8-9
0%
7-8
0%
6-7
100%
0-6
0%

DETAILED RATINGS

Content Quality
6.0
Instructor Delivery
6.0
Platform Experience
7.0
Value For Money
5.0
Learning Outcome
6.0
All reviews are verified by TrustCourse

Course Syllabus

1

Module 1 • Introduction to Machine Learning

2

Module 2 • Training and Test Sets

3

Module 3 • Cross-validation

4

Module 4 • Linear Regression

5

Module 5 • Smoothing and Local Regression

6

Module 6 • Classification

7

Module 7 • Recommendation Systems

8

Module 8 • Regularization and Matrix Factorization

TrustCourse AI AnalysisAI Generated
Based on public data analysis
•
Updated May 29, 2025
6.0/10
Average

Course Goal

Build a movie recommendation system and learn popular machine learning algorithms, principal component analysis, and regularization techniques using R programming

One-Minute Verdict

Solid intro to ML with R but overpriced at $149. Good for Harvard brand recognition but lacks depth in explanations compared to expectations.

PROS

  • Harvard University brand recognition and credibility
  • Learn R programming from scratch with practical movie recommendation project
  • Part of comprehensive Professional Certificate program
  • Over 636k students enrolled showing popularity

CONS

  • Overpriced at $149 compared to alternatives and previous pricing
  • Explanations sometimes insufficient for new concepts
  • Less rigorous than expected for Harvard-level course
  • Limited depth in statistical theory compared to practical coding

DETAILED AI RATINGS

Content Quality
6.0/10

Covers ML fundamentals and R programming through movie recommendation project. Some students report explanations lack depth and feel rushed.

Instructor Delivery
6.0/10

Prof Irizarry leads comprehensive program but students report insufficient explanations of new concepts and code examples.

Platform Experience
7.0/10

Standard edX platform with reliable video delivery, discussion boards, and lifetime access to course materials.

Value For Money
5.0/10

Criticized as overpriced at $149, especially after price increase from previous $50. Harvard brand premium questioned.

Learning Outcome
6.0/10

Students learn R programming and basic ML concepts but report mixed retention of statistical fundamentals and theory.

DECISION GUIDE

Take This Course If

You want Harvard credentials and need to learn R programming from beginner level

Good for resume building with recognized university brand and comprehensive R introduction through practical ML project.

Skip This Course If

You're looking for deep statistical theory or have budget constraints

Course prioritizes practical coding over theoretical depth and costs significantly more than comparable alternatives.

SOURCES & REFERENCES

  • https://www.reddit.com/r/edX/comments/1dwp35b/harvardxs_data_science_professional_certificate/
  • https://www.edx.org/learn/machine-learning/harvard-university-data-science-machine-learning
  • https://www.reddit.com/r/edX/comments/skqght/harvardx_data_science_is_completely_overpriced/
  • https://www.linkedin.com/posts/sam-mirazi_harvardx-machinelearning-ai-activity-7251462642840584192-442s
  • https://www.linkedin.com/posts/joanne-kenneyphd_datascience-machinelearning-harvardx-activity-7276918939165626368-HWNz
  • https://www.reddit.com/r/edX/comments/ju4jy0/harvard_or_mit_data_science_course/
  • https://github.com/dwyl/learn-machine-learning/issues/18
  • https://www.linkedin.com/pulse/my-harvard-edx-data-science-course-experience-sarah-mancinho
  • https://www.reddit.com/r/edX/comments/1cmf7m8/data_science_certcourse_recommendations/

About this AI Summary

This summary is generated by AI based on public internet data, including social networks and available articles.

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