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Full Stack Data Science & Machine Learning BootCamp Course

Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects

  1. Topics
  2. Development
  3. SQL & Database Fundamentals

Full Stack Data Science & Machine Learning BootCamp Course

InstructorAkhil Vydyula
Duration34h 27m
Students16K
Rating4.4 (82)
Sponsored
Price
$0.00
$17.99
Coupon
99/100 uses left
Last checked 6d ago
Access
Premium only
Upgrade to unlock this course deal

Coupon history

Comidoc has tracked 15 coupons for this course since 2025, last checked 6d ago. On average, a new coupon appears roughly every 21 days.

Coupon codeDiscountAddedStatusLifetime
AUG-FREE100% offAug 2, 202606:41 PM UTCExpired29d 12h
JUL-FREE100% offJul 1, 202604:03 PM UTCExpired~5 daysRan full term
JUN-FREE100% offJun 6, 202612:40 PM UTCExpired~26 daysRan full term
SIRILOVE100% offMay 24, 202611:04 PM UTCExpired~21 daysRan full term
MAY-FREE100% offMay 10, 202603:50 PM UTCExpired~22 daysRan full term
SIRISHALIFE100% offApr 13, 202611:11 AM UTCExpired~28 daysRan full term
Comidoc Analysis

Wide-ranging Data Science and Machine Learning curriculum

Strengths

Diverse Project-Led Learning

The curriculum includes multiple capstone projects such as flight fare prediction, mushroom classification, and toxic comment classification to build a practical portfolio.

Broad Toolset Integration

Instruction extends beyond machine learning to include SQL database management and business intelligence via Microsoft Power BI.

Editorial course preview

What the public course preview actually shows

This view highlights a concrete, legible example from the course presentation.

Preview 1 of 1

This diagram contrasts the unbounded linear regression line with the bounded sigmoid curve of logistic regression, displaying both equations for clarity.

Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.

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Limitations

Variable Instructional Clarity

Learner signals indicate inconsistent explanation quality, with some finding the instruction difficult to follow or lacking specific library introductions.

Rapid Model Implementation

The pace of building models can feel accelerated, leading to requests for more granular examples and practice opportunities.

Best suited to

  • Career changers seeking an end-to-end data science overview
  • Learners wanting to build a project-led portfolio
  • Beginners looking for integrated SQL and BI tool instruction

Less suited to

  • Students seeking deep theoretical statistical rigor
  • Learners requiring highly granular Python library tutorials

Comidoc Score

5.9/10

Worth considering
Beginner-friendly

Comidoc verdict

Python fundamentals lead into machine learning and deep learning, concluding with SQL and Power BI. This structure provides a wide-sights view of the data science workflow, supported by several capstone projects designed to build a functional portfolio.

The breadth of topics is a primary strength, though instructional consistency varies. Some learners report difficulty understanding specific explanations or suggest that more detailed coverage of Python libraries would be beneficial. Additionally, the project-led approach can feel accelerated, with some signals indicating a desire for more frequent practice examples to reinforce model building.

This course is best suited for career changers and beginners who want a broad, end-to-end introduction to the various tools used in data science roles.

Score breakdown

Curriculum depth
6.5

The curriculum covers a wide range of topics including statistics, SQL, and deep learning architectures, though some signals suggest more depth in specific libraries could be useful.

Applied learning
8.0

The inclusion of multiple capstone projects across different machine learning tasks provides significant hands-on application.

Clarity & experience
3.5

Instructional signals are mixed; while some find the path engaging, others report difficulty understanding explanations or a lack of detail in library introductions.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
5.8

The curriculum aligns well with the stated goal of taking beginners through a full-stack data science path.

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