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