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Machine Learning Practical Workout | 8 Real-World Projects

Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks

  1. Topics
  2. Development
  3. Machine Learning (ML)

Machine Learning Practical Workout | 8 Real-World Projects

InstructorProf. Ryan Ahmed, Ph.D., MBA | 600,000+ Students | Best-Selling Instructor
Duration14h 14m
Students22.3K
Rating4.5 (2,167)

More Related Topics

  • Deep Learning (DL)602
  • Advanced Neural Networks185
  • Applied Data Science31
  • Data Science Fundamentals851
  • Artificial Intelligence Basics2117
  • Python for Data Analysis2077
  • Python Programming3160
  • Data Science Fundamentals200
  • Scikit-learn for ML60
  • Azure AI Fundamentals81
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Comidoc Analysis

Diverse project applications with significant technical maintenance concerns

Strengths

Diverse Project Scope

The curriculum covers a wide range of domains including image classification, time series forecasting, natural language processing, and collaborative filtering.

Accessible Concept Explanations

One sampled review suggests that some learners find the explanations of core concepts to be clean and easy to understand.

Limitations

Technical Debt and Broken Links

Evidence indicates that external links, such as Dropbox, may be broken and that provided code could be outdated.

Simplified Project Complexity

Certain use cases are described as oversimplified, utilizing single inputs that do not reflect the multi-feature complexity of real-world scenarios.

Best suited to

  • Beginners seeking a broad introduction to ML projects
  • Learners interested in time series forecasting with Prophet

Less suited to

  • Intermediate users requiring rigorous theoretical depth
  • Learners needing highly complex, multi-feature real-world datasets

Comidoc Score

5.6/10

Worth considering
Beginner-friendly

Comidoc verdict

The curriculum moves from environment setup into specific case studies involving neural networks, Prophet time series, and NLP. This structure allows for exposure to different machine learning techniques through hands-on application.

However, the practical value is challenged by reports of broken resource links and code that may no longer align with current standards. Some projects are noted as being too simplified for high-level application, functioning more as basic code-alongs than rigorous technical challenges.

Editorial course preview

What the public course preview actually shows

These 2 complementary views highlight concrete, legible examples from the course presentation.

Preview 1 of 2

The instructor introduces the practical application of machine learning by discussing how to build prediction models for real-world scenarios like forecasting car sales and tracking avocado prices.

Preview 2 of 2

An instructor introduces the topic of artificial neural networks, standing before a whiteboard diagram illustrating biological neuron structure.

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

This course is best suited for beginners looking for a broad overview of various machine learning tasks, provided they can navigate potential technical issues with the provided materials.

Score breakdown

Curriculum depth
5.8

The curriculum covers a wide range of topics, but signals suggest the depth may be limited for intermediate users.

Applied learning
5.8

The course is heavily project-based, though some projects are criticized for lacking complexity and rigor.

Clarity & experience
5.8

Instructional clarity is mixed; while some find explanations easy to follow, others report incomplete content.

Currency & reliability
4.3

Reports of broken links and outdated code suggest the material may require more frequent maintenance.

Audience fit
6.5

The curriculum aligns well with the stated goal of providing a practical introduction for beginners.