Extrusion machine operator education requirements

1. Material Extrusion

This course is a deep dive into the most commonly used method of additive manufacturing: material extrusion. We specifically examine the fused deposition modeling process, and then look at the related composite extrusion process which can extrude a continuous fiber (such as carbon fiber) along with a thermoplastic. The necessary elements for each course are lecture videos, knowledge checks, and project completion. For additional information on certain topics, I've included supplemental readings...

2. Build and Operate Machine Learning Solutions with Azure

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate...

3. Executive Briefing: Machine Learning

4.1

(5,211)

Machine Learning for Executives and Top-Level Managers: understand Machine Learning in under 90 Minutes...

4. Machine Learning Classification Bootcamp in Python

4.5

(934)

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn...

5. Machine Learning Engineering for Production (MLOps)

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.\n\nEffectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and...

6. Machine Learning with PySpark: Customer Churn Analysis

This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark. We are going to use our machine...

7. The Complete Machine Learning Course with Python

4.5

(6,881)

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!...

8. Machine Learning with Javascript

4.7

(3,132)

Master Machine Learning from scratch using Javascript and Tensorflow JS with hands-on projects...

9. DP-100: A-Z Machine Learning using Azure Machine Learning

4.4

(6,728)

Microsoft Azure DP-100: Designing and Implementing a Data Science Solution Exam Covered. Learn Azure Machine Learning...

10. Mathematical Foundation For Machine Learning and AI

4.6

(1,262)

Learn the core mathematical concepts for machine learning and learn to implement them in R and python...

11. Deployment of Machine Learning Models

4.5

(4,961)

Learn how to integrate robust and reliable Machine Learning Pipelines in Production...

12. Azure Machine Learning & MLOps: Beginner to Advance

4.4

(465)

The most compressive course covering MLOps and machine learning on Azure Zero to Hero...

13. Machine Learning No-Code Approach: Using Azure ML Studio

4.9

(542)

A hands-on approach to Machine Learning using the easy drag-n-drop environment of Azure Machine Learning Studio...

14. Machine Learning Product Management: A Practical Guide 2023

4.5

(268)

Kick start your career in Machine Learning Product Management with just one course...

15. Projects in Machine Learning: Beginner To Professional

4.3

(591)

A complete guide to master machine learning concepts and create real world ML solutions...

16. Introduction to Machine Learning for Data Science

4.6

(13,692)

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist...

17. Machine Learning Practical: 6 Real-World Applications

4.5

(2,836)

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python...

18. Machine Learning for Data Analysis: Unsupervised Learning

4.8

(282)

Machine Learning made simple with Excel! Unsupervised learning topics for advanced data analysis & business intelligence...

19. Mathematical Foundations of Machine Learning

4.5

(5,125)

Essential Linear Algebra and Calculus Hands-On in Num Py, Tensor Flow, and Py Torch...