Udemy

End-to-End Machine Learning: From Idea to Implementation

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  • 8,677 Students
  • Updated 2/2025
  • Certificate Available
4.9
(396 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Kıvanç Yüksel
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.9
(396 Ratings)
1 views

Course Overview

End-to-End Machine Learning: From Idea to Implementation

Build, Manage, and Deploy Machine Learning (AI) Projects with Python and MLOps

Embark on a hands-on journey to mastering Machine Learning project development with Python and MLOps. This course is meticulously crafted to equip you with the essential skills required to build, manage, and deploy real-world Machine Learning projects.


With a focus on practical application, you'll dive into the core of MLOps (Machine Learning Operations) to understand how to streamline the lifecycle of Machine Learning projects from ideation to deployment. Discover the power of Python as the driving force behind the efficient management and operationalization of Machine Learning models.


Engage with a comprehensive curriculum that covers data versioning, distributed data processing, feature extraction, model training, evaluation, and much more. The course also introduces you to essential MLOps tools and practices that ensure the sustainability and scalability of Machine Learning projects.


Work on a capstone project that encapsulates all the crucial elements learned throughout the course, providing you with a tangible showcase of your newfound skills. Receive constructive feedback and guidance from an experienced instructor dedicated to helping you succeed.


Join a vibrant community of like-minded learners and professionals through our interactive platform, and kickstart a rewarding journey into the dynamic world of Machine Learning projects powered by Python and MLOps. By the end of this course, you'll have a solid foundation, practical skills, and a powerful project in your portfolio that demonstrates your capability to lead Machine Learning projects to success.


Enroll today and take a significant step towards becoming proficient in developing and deploying Machine Learning projects using Python and MLOps. Your adventure into the practical world of Machine Learning awaits!

Course Content

  • 18 section(s)
  • 277 lecture(s)
  • Section 1 Introduction
  • Section 2 Git and Github Quickstart
  • Section 3 Docker Quickstart
  • Section 4 DVC
  • Section 5 Hydra
  • Section 6 Google Cloud Platform Quickstart
  • Section 7 MLFlow
  • Section 8 Dask
  • Section 9 Launching Jobs on Google Cloud Platform for Distributed Model Training
  • Section 10 FastAPI
  • Section 11 Streamlit
  • Section 12 Project Environment Setup
  • Section 13 Data Versioning with DVC
  • Section 14 Data Processing
  • Section 15 Training a Tokenizer
  • Section 16 Distributed Model Training and Evaluation
  • Section 17 Web App Creation
  • Section 18 Wrapping Up

What You’ll Learn

  • How To Efficiently Build Sustainable And Scalable Machine Learning Projects Using The Best Practices
  • Data Versioning
  • Distributed Data Processing
  • Feature Extraction
  • Distributed Model Training
  • Model Evaluation
  • Experiment Tracking
  • Error analysis
  • Model Inference
  • Creating An Application Using The Model We Train
  • Metadata management
  • Reproducibility
  • MLOps
  • MLOps principals
  • Machine Learning Operations
  • Machine Learning
  • Deep Learning
  • Artificial Intelligence
  • AI

Reviews

  • C
    Carol Ray
    5.0

    Great content

  • L
    Logan Chambers
    5.0

    Thank you for the course, learned a lot!

  • E
    Emerson Fry
    5.0

    Truly amazing course!

  • R
    Rajesh Sidhu
    5.0

    Excellent course! Covers the full ML pipeline with clear, practical examples.

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