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HomeCoursesData Science & AnalyticsMachine Learning Specialization

Machine Learning Specialization

By Coursera with Stanford | DeepLearning.AI

Data Science & Analytics

8.2
•1 verified reviewTrustScore
Our ratings use a 10-star system for more precise quality assessment
Specialization
94 hours
Beginner
Certificate Included
Visit Provider
Write a ReviewWrite a Story
TrustCourse Rating
Verified
B+
TRUST·SCORE
8.2
OUT OF 10
1 verified review
Very Good
Rating Distribution% of reviews
9-10
0%
8-9
100%
7-8
0%
6-7
0%
0-6
0%

DETAILED RATINGS

Content Quality
8.0
Instructor Delivery
9.0
Platform Experience
8.0
Value For Money
9.0
Learning Outcome
7.0
All reviews are verified by TrustCourse

Course Syllabus

1

Course 1 • Supervised Learning: Regression & Classification

2

Course 2 • Advanced Learning Algorithms & Neural Networks

3

Course 3 • Unsupervised Learning, Recommenders & Reinforcement Learning

TrustCourse AI AnalysisAI Generated
Based on public data analysis
•
Updated May 26, 2025
8.2/10
Very Good

Course Goal

Master fundamental AI concepts and develop practical machine learning skills through hands-on Python programming with NumPy, scikit-learn, and TensorFlow

One-Minute Verdict

Gold standard beginner ML course with excellent teaching, but insufficient alone for ML jobs—expect to supplement with projects and advanced material.

PROS

  • World-class instructor with exceptional teaching clarity
  • Updated Python-based curriculum (previously MATLAB)
  • Comprehensive beginner-friendly coverage of core ML concepts

CONS

  • Math-light approach may leave gaps for advanced applications
  • Insufficient alone for immediate job market competitiveness

DETAILED AI RATINGS

Content Quality
8.0/10

Comprehensive coverage from regression to neural networks. Math-light approach may lack depth for advanced applications.

Instructor Delivery
9.0/10

Andrew Ng's clear, engaging explanations consistently praised. Excellent at making complex concepts accessible.

Platform Experience
8.0/10

Coursera's reliable platform with interactive notebooks and flexible deadlines. Good video quality and navigation.

Value For Money
9.0/10

At $49/month for 94 hours of premium content from renowned instructor, excellent value for foundational learning.

Learning Outcome
7.0/10

Strong foundational understanding achieved. Students report confidence boost but need additional training for job readiness.

DECISION GUIDE

Take This Course If

You're new to ML or transitioning from other fields

Perfect foundation course that makes complex concepts accessible without overwhelming mathematical prerequisites.

Skip This Course If

You already have ML experience or need immediate job-ready skills

Content may be too basic, and you'll need additional practical projects and advanced courses for employment.

SOURCES & REFERENCES

  • https://www.coursera.org/specializations/machine-learning-introduction
  • https://www.reddit.com/r/learnmachinelearning/comments/vi6ub0/what_do_you_think_of_andrew_ngs_new_machine/
  • https://www.youtube.com/watch?v=laXNsRX2L3M
  • https://www.kdnuggets.com/5-top-machine-learning-courses-you-can-take-in-2024
  • https://www.reddit.com/r/learnmachinelearning/comments/1abldab/is_coursera_machine_learning_specialization_by/
  • https://www.getbridged.co/course-review/stanford-deepai-machine-learning-specialization
  • https://www.youtube.com/watch?v=5gf2ntjVGe8
  • https://www.reddit.com/r/learnmachinelearning/comments/1clazck/courseras_machine_learning_specialization_vs_mits/
  • https://community.deeplearning.ai/t/what-organization-to-put-in-linkedin-for-the-ml-and-dl-specializations/525223
  • https://twm.me/coursera-deep-learning-specialization-andrew-ng-review/

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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