ProductizeML
  • ProductizeML
  • Introduction
    • Objectives
    • About the Course
    • Guidelines
    • Syllabus
    • After Completion
  • Machine Learning
    • Why ML, and why now
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • ML Terminology
  • Data Management
    • Data Access
    • Data Collection
    • Data Curation
  • Train and Evaluate
    • Framework and Hardware
    • Training Neural Networks
    • Model Evaluation
  • Productize It
    • ML Life Cycle
    • Business Objectives
    • Data Preparation
    • Model Development
    • Train, Evaluate, and Deploy
    • A/B Testing
    • KPI Evaluation
    • PM Terminology
  • Resources
    • Readings
    • Courses
    • Videos
  • Hands-On
    • Python for Machine Learning
      • Python Installation
        • MacOS
        • Linux
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  • About the course
  • Why ProductizeML?
  • Course Lectures
  • Lectures Breakdown
  • About us

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ProductizeML

A self-study guide for teams building Artificial Intelligence and Machine Learning products.

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Last updated 2 years ago

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About the course

ProductizeML provides guidelines and best practices for managing the end-to-end Machine Learning life cycle and its productization.

The course will start diving into the fundamentals around the Machine Learning algorithms, from the supervised and unsupervised learning strategies to the latest advances in deep learning techniques; followed by some recommended ways of accessing and managing data. Tips and best practices when training and evaluating models will be provided. And last but not least, the core piece of this course: how to bring it to production and make an outstanding product out of it!

Why ProductizeML?

  • Learn to develop an ML product that can be included in your business solution.

  • Join the community to interact and share with mentors and fellow students.

  • Manage your time to go through the course material at your own pace.

  • Practice with real-world examples built by industry experts.

This course is constantly growing and expanding, meaning that some sections might be under construction 🚧 — do NOT panic, and instead leave a message of what you would like to see!

Course Lectures

Lectures Breakdown

About us

For more information on how to start this course, please read the course's**** and****.

Join the community at and follow us on !

Have a question or suggestion? You can reach out at .

Objectives
Guidelines
Why ML, and why now
Supervised Learning
Unsupervised Learning
Deep Learning
Data Access
Data Collection
Data Curation
Framework and Hardware
Training Neural Networks
Model Evaluation
ML Life Cycle
Business Objectives
Data Preparation
Model Development
Train, Evaluate, and Deploy
A/B Testing
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