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

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.

What's inside

Learning objectives

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
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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
Show more
Show less

Syllabus

Introduction

Why This Course?

Why Too Many Companies Fail?

Why Too Many Companies Fail - Resources

Tips To Improve Your Course Taking Experience

Discord Server

Where to start?

Lecture Slides

A Note For Windows Users

In the rest of the course we are going to use Git and Github extensively. If you are not familiar with it, you can learn all the necessary things you need to know from this section.

Git and Github Quickstart section introduction

Git and Github - What are they?

Git Installation - Linux

Git Installation - Windows

Git Installation - MacOS

Github - Account creation

Adding an SSH key pair to GitHub account - Linux

Adding an SSH key pair to GitHub Account - MacOS

Adding an SSH key pair to GitHub account - Windows

Git and GitHub - Basic workflow

Reverting Your Changes Back

Commit History

Aliases

Reverting Back to a Previous Commit

Git Diff

Branching and Merging

Pull Request and Code Review

Rebase

Stashing

Tagging

Cherry Pick

Git and GitHub - Final Words

In the rest of the course we are going to use Docker extensively. If you are not familiar with it, you can learn all the necessary things you need to know from this section.

Docker Quickstart section introduction

What Is Docker and Why Do We Use It?

Installation - Linux

Installation - Windows

Installation - MacOS

A Note For NVIDIA GPU Users

Docker Containers

Docker Containers - Hands On

Why Docker Is So Good?

Docker Images

Dockerfile

More about Dockerfile

Persistent Data In Docker

Persistent Data In Docker - Volumes - Hands On

Persistent Data in Docker - Bind Mounting - Hands On

Docker Compose

Dockerfile Best Practices

Data versioning and pipelines using DVC

DVC - Section Introduciton

Data Versioning

Accessing Your Data

Pipelines - Part 1

Pipelines - Part 2

Pipelines - Part 3

Metrics And Experiments

Hydra

Hydra - Section Introduction

How to Use Hydra From Command-Line?

Specifying A Config File

More About OmegaConf

Grouping Config Files

Selecting Default Configs

Multirun

Output And Working Directory

Logging

Debugging

Instantiate

Packages

A Small Project To See "The Big Picture"

Small Project - Assignment

Small Project - Assignment Solution

Tab Completion

Structured Configs

Structured Configs Basic Usage

Hierarchical Static Configuration

Config Groups in Structured Configs - Part 1

Config Groups in Structured Configs - Part 2

Defaults List in Structured Configs

Structured Config Schema

Validating Config Parameters Using Pydantic

Extending The Small Project With Structured Configs

Extending The Small Project With Structured Configs - Course Assignment

Extending The Small Project With Structured Configs - Assignment Solution

Google Cloud Platform Quickstart

Google Cloud Platform - Section Introduction

How to Create An Account?

How to Create a Project?

"gsutils" and "gcloud" commands

A Note About "gsutils" and "gcloud" commands

Google Cloud Storage (GCS) - Bucket Creation

Google Cloud Storage (GCS) - Bucket Usage

Section Checkpoint

Google Compute Engine (GCE)

Google Compute Engine (GCE) - Quotas

Artifact Registry

Firewall Rules

Instance Groups

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for those in higher education who want to build their career in Machine Learning

Ideal for learners seeking to build end-to-end skills in Machine Learning project development and MLOps

Incorporates hands-on projects to apply MLOps concepts in real-world scenarios

May require prior familiarity with Python for optimal learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in End-to-End Machine Learning: From Idea to Implementation with these activities:

Brush up on Python basics

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Solidify your understanding of the core fundamentals of Python programming, making it easier to grasp the advanced concepts covered in this course.

Browse courses on Data Versioning

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Review essential Python syntax and data types
Practice writing simple Python scripts
Explore basic object-oriented programming concepts in Python

Read 'Machine Learning with Python: A Practical Introduction'

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Gain a comprehensive understanding of machine learning concepts and their practical application in Python, providing a solid foundation for the advanced topics covered in this course.

View Machine Learning with PyTorch and Scikit-Learn:... on Amazon

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Read Chapter 1-3 to grasp the fundamentals of machine learning
Work through the examples and exercises in the book

Attend a local MLOps meetup or conference

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Connect with professionals in the field, exchange ideas, and learn about the latest trends in MLOps.

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Research and identify relevant MLOps events in your area
Register for and attend the event
Engage in discussions and networking with attendees

Six other activities

Expand to see all activities and additional details

Show all nine activities

Create a cheat sheet of MLOps principles

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Develop a concise reference guide that summarizes the key principles and practices of MLOps, reinforcing your understanding and providing a handy resource for future reference.

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Identify and list the core principles of MLOps
Summarize best practices for each principle
Organize and format the cheat sheet for easy use

Join a study group or online forum focused on MLOps

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Engage with peers, share knowledge, and discuss challenges, fostering a collaborative learning environment.

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Identify and join an online forum or study group related to MLOps
Actively participate in discussions and ask questions
Collaborate on projects or assignments with other members

Complete the 'MLOps with TensorFlow Extended (TFX)' tutorial

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Get hands-on practice with a real-world MLOps pipeline, deepening your understanding of the concepts and their implementation.

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Follow the official TensorFlow Extended (TFX) tutorial
Experiment with different pipeline components and settings

Solve MLOps-related coding challenges on LeetCode

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Sharpen your problem-solving skills and reinforce your understanding of MLOps concepts by tackling real-world coding challenges.

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Identify and solve LeetCode problems related to MLOps
Analyze and discuss your solutions with peers or online communities

Contribute to an open-source MLOps project on GitHub

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Gain practical experience and demonstrate your skills by contributing to a real-world MLOps project.

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Identify an open-source MLOps project that aligns with your interests
Fork the project and make changes
Submit a pull request and engage with the project maintainers

Develop a small-scale MLOps project showcasing your skills

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Apply your knowledge and skills to create a tangible project that demonstrates your proficiency in MLOps.

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Define the scope and objectives of your project
Design and implement an MLOps pipeline
Deploy and monitor your model

Career center

Learners who complete End-to-End Machine Learning: From Idea to Implementation will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This course can help you become a Machine Learning Engineer by teaching you the skills necessary to build, manage, and deploy real-world Machine Learning projects. You will learn how to use Python and MLOps to streamline the lifecycle of Machine Learning projects from ideation to deployment.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses data to solve business problems. This course can help you become a Data Scientist by teaching you the skills necessary to work with data, build machine learning models, and communicate your findings to stakeholders. You will learn how to use Python and MLOps to manage and operationalize Machine Learning models.

See salaries and explore the career path for Data Scientist

Machine Learning Researcher

A Machine Learning Researcher develops new machine learning algorithms and techniques. This course can help you become a Machine Learning Researcher by teaching you the skills necessary to conduct research in machine learning. You will learn how to use Python and MLOps to manage and operationalize machine learning research projects.

See salaries and explore the career path for Machine Learning Researcher

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course can help you become a Software Engineer by teaching you the skills necessary to build and deploy software applications. You will learn how to use Python and MLOps to manage and operationalize software systems.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course can help you become a Data Analyst by teaching you the skills necessary to work with data, build machine learning models, and communicate your findings to stakeholders.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst helps businesses understand their needs and develop solutions to improve their operations. This course can help you become a Business Analyst by teaching you the skills necessary to work with stakeholders, understand business processes, and develop solutions to business problems.

See salaries and explore the career path for Business Analyst

Product Manager

A Product Manager develops and manages products to meet the needs of users. This course can help you become a Product Manager by teaching you the skills necessary to understand user needs, develop product roadmaps, and manage product development.

See salaries and explore the career path for Product Manager

Project Manager

A Project Manager plans, executes, and closes projects. This course can help you become a Project Manager by teaching you the skills necessary to manage projects, teams, and stakeholders.

See salaries and explore the career path for Project Manager

Technical Writer

A Technical Writer creates documentation for software and other technical products. This course can help you become a Technical Writer by teaching you the skills necessary to write clear and concise documentation.

See salaries and explore the career path for Technical Writer

Data Engineer

A Data Engineer builds and maintains data pipelines to support data-driven decision-making. This course may be useful for you if you are interested in becoming a Data Engineer by teaching you the skills necessary to work with data, build data pipelines, and manage data infrastructure.

See salaries and explore the career path for Data Engineer

DevOps Engineer

A DevOps Engineer automates and streamlines the software development and deployment process. This course may be useful for you if you are interested in becoming a DevOps Engineer by teaching you the skills necessary to work with software development teams, build and deploy software applications, and manage infrastructure.

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Cloud Engineer

A Cloud Engineer designs, builds, and maintains cloud computing systems. This course may be useful for you if you are interested in becoming a Cloud Engineer by teaching you the skills necessary to work with cloud computing platforms, build and deploy cloud applications, and manage cloud infrastructure.

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Systems Engineer

A Systems Engineer designs, builds, and maintains computer systems. This course may be useful for you if you are interested in becoming a Systems Engineer by teaching you the skills necessary to work with computer hardware and software, build and deploy computer systems, and manage system infrastructure.

See salaries and explore the career path for Systems Engineer

Network Engineer

A Network Engineer designs, builds, and maintains computer networks. This course may be useful for you if you are interested in becoming a Network Engineer by teaching you the skills necessary to work with network hardware and software, build and deploy computer networks, and manage network infrastructure.

See salaries and explore the career path for Network Engineer

Database Administrator

A Database Administrator designs, builds, and maintains databases. This course may be useful for you if you are interested in becoming a Database Administrator by teaching you the skills necessary to work with database software, build and deploy databases, and manage database infrastructure.

See salaries and explore the career path for Database Administrator