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

  • Development
  • Mar 11, 2025
SynopsisEnd-to-End Machine Learning: From Idea to Implementation, ava...
End-to-End Machine Learning- From Idea to Implementation  No.1

End-to-End Machine Learning: From Idea to Implementation, available at $89.99, has an average rating of 4.68, with 317 lectures, based on 237 reviews, and has 6491 subscribers.

You will learn about 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 This course is ideal for individuals who are Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices or Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run or Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable or Researchers who are interested in developing machine learning models more efficiently or Software developers who want to gain expertise in developing sustainable and scalable machine learning projects or Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable or Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects or Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development or Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability It is particularly useful for Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices or Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run or Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable or Researchers who are interested in developing machine learning models more efficiently or Software developers who want to gain expertise in developing sustainable and scalable machine learning projects or Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable or Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects or Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development or Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability.

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

Summary

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

Price: $89.99

Average Rating: 4.68

Number of Lectures: 317

Number of Published Lectures: 277

Number of Curriculum Items: 317

Number of Published Curriculum Objects: 277

Original Price: $149.99

Quality Status: approved

Status: Live

What You Will 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
  • Who Should Attend

  • Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices
  • Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run
  • Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable
  • Researchers who are interested in developing machine learning models more efficiently
  • Software developers who want to gain expertise in developing sustainable and scalable machine learning projects
  • Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable
  • Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects
  • Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development
  • Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability
  • Target Audiences

  • Students who are interested in pursuing a career in machine learning project development and want to gain expertise in sustainable and scalable development practices
  • Machine learning engineers who are interested in developing machine learning solutions that are scalable and sustainable in the long run
  • Data scientists who are looking to expand their skill set to include machine learning project development that is scalable and sustainable
  • Researchers who are interested in developing machine learning models more efficiently
  • Software developers who want to gain expertise in developing sustainable and scalable machine learning projects
  • Start-up founders who want to develop machine learning projects that can be scaled up to meet future demands while also being sustainable
  • Technical project managers who want to learn how to effectively manage and oversee sustainable and scalable machine learning projects
  • Professionals in the technology industry who want to stay up-to-date with the latest trends and advancements in machine learning project development
  • Companies and organizations that want to implement sustainable and scalable machine learning projects to improve their operations, efficiency, and profitability
  • 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 Curriculum

    Chapter 1: Introduction

    Lecture 1: Why This Course?

    Lecture 2: Why Too Many Companies Fail?

    Lecture 3: Why Too Many Companies Fail – Resources

    Lecture 4: Tips To Improve Your Course Taking Experience

    Lecture 5: Discord Server

    Lecture 6: Where to start?

    Lecture 7: Lecture Slides

    Lecture 8: A Note For Windows Users

    Chapter 2: Git and Github Quickstart

    Lecture 1: Git and Github Quickstart section introduction

    Lecture 2: Git and Github – What are they?

    Lecture 3: Git Installation – Linux

    Lecture 4: Git Installation – Windows

    Lecture 5: Git Installation – MacOS

    Lecture 6: Github – Account creation

    Lecture 7: Adding an SSH key pair to GitHub account – Linux

    Lecture 8: Adding an SSH key pair to GitHub Account – MacOS

    Lecture 9: Adding an SSH key pair to GitHub account – Windows

    Lecture 10: Git and GitHub – Basic workflow

    Lecture 11: Reverting Your Changes Back

    Lecture 12: Commit History

    Lecture 13: Aliases

    Lecture 14: Reverting Back to a Previous Commit

    Lecture 15: Git Diff

    Lecture 16: Branching and Merging

    Lecture 17: Pull Request and Code Review

    Lecture 18: Rebase

    Lecture 19: Stashing

    Lecture 20: Tagging

    Lecture 21: Cherry Pick

    Lecture 22: Git and GitHub – Final Words

    Chapter 3: Docker Quickstart

    Lecture 1: Docker Quickstart section introduction

    Lecture 2: What Is Docker and Why Do We Use It?

    Lecture 3: Installation – Linux

    Lecture 4: Installation – Windows

    Lecture 5: Installation – MacOS

    Lecture 6: A Note For NVIDIA GPU Users

    Lecture 7: Docker Containers

    Lecture 8: Docker Containers – Hands On

    Lecture 9: Why Docker Is So Good?

    Lecture 10: Docker Images

    Lecture 11: Dockerfile

    Lecture 12: More about Dockerfile

    Lecture 13: Persistent Data In Docker

    Lecture 14: Persistent Data In Docker – Volumes – Hands On

    Lecture 15: Persistent Data in Docker – Bind Mounting – Hands On

    Lecture 16: Docker Compose

    Lecture 17: Dockerfile Best Practices

    Chapter 4: DVC

    Lecture 1: DVC – Section Introduciton

    Lecture 2: Data Versioning

    Lecture 3: Accessing Your Data

    Lecture 4: Pipelines – Part 1

    Lecture 5: Pipelines – Part 2

    Lecture 6: Pipelines – Part 3

    Lecture 7: Metrics And Experiments

    Chapter 5: Hydra

    Lecture 1: Hydra – Section Introduction

    Lecture 2: How to Use Hydra From Command-Line?

    Lecture 3: Specifying A Config File

    Lecture 4: More About OmegaConf

    Lecture 5: Grouping Config Files

    Lecture 6: Selecting Default Configs

    Lecture 7: Multirun

    Lecture 8: Output And Working Directory

    Lecture 9: Logging

    Lecture 10: Debugging

    Lecture 11: Instantiate

    Lecture 12: Packages

    Lecture 13: A Small Project To See The Big Picture

    Lecture 14: Small Project – Assignment

    Lecture 15: Small Project – Assignment Solution

    Lecture 16: Tab Completion

    Lecture 17: Structured Configs

    Lecture 18: Structured Configs Basic Usage

    Lecture 19: Hierarchical Static Configuration

    Lecture 20: Config Groups in Structured Configs – Part 1

    Lecture 21: Config Groups in Structured Configs – Part 2

    Lecture 22: Defaults List in Structured Configs

    Lecture 23: Structured Config Schema

    Lecture 24: Validating Config Parameters Using Pydantic

    Lecture 25: Extending The Small Project With Structured Configs

    Lecture 26: Extending The Small Project With Structured Configs – Course Assignment

    Lecture 27: Extending The Small Project With Structured Configs – Assignment Solution

    Chapter 6: Google Cloud Platform Quickstart

    Lecture 1: Google Cloud Platform – Section Introduction

    Lecture 2: How to Create An Account?

    Lecture 3: How to Create a Project?

    Lecture 4: gsutils and gcloud commands

    Lecture 5: A Note About gsutils and gcloud commands

    Lecture 6: Google Cloud Storage (GCS) – Bucket Creation

    Lecture 7: Google Cloud Storage (GCS) – Bucket Usage

    Lecture 8: Section Checkpoint

    Lecture 9: Google Compute Engine (GCE)

    Lecture 10: Google Compute Engine (GCE) – Quotas

    Lecture 11: Artifact Registry

    Lecture 12: Firewall Rules

    Lecture 13: Instance Groups

    Instructors

  • End-to-End Machine Learning- From Idea to Implementation  No.2
    K?van? Yüksel
    Machine Learning Researcher / Engineer / Enthusiast
  • Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 3 votes
  • 3 stars: 7 votes
  • 4 stars: 53 votes
  • 5 stars: 171 votes
  • Frequently Asked Questions

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