Fiberglass machine operator education requirements

1. 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...

2. Optimizing Machine Learning Performance

This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of...

3. Machine Learning Optimization Using Genetic Algorithm

4.1

(341)

Learn how to optimize Machine Learning algorithms' performances and apply feature selection using Genetic Algorithm...

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 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...

6. 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!...

7. Machine Learning with Javascript

4.7

(3,132)

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

8. Productivity Machine: Time Management & Productivity Hacks

4.5

(27,996)

Productivity & Time Management Strategies for Goal Setting, Eliminating Distractions, & Building Habits for Success...

9. Machine Learning Product Management: A Practical Guide 2023

4.5

(268)

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

10. 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...

11. Google Cloud Machine Learning Engineer Certification Prep

4.5

(434)

Building, Deploying, and Managing Machine Learning Services at Scale...

12. Machine Learning Practical: 6 Real-World Applications

4.5

(2,836)

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

13. Deployment of Machine Learning Models

4.5

(4,961)

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

14. Executive Briefing: Machine Learning

4.1

(5,211)

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

15. Excel for Data Science and Machine Learning

4.5

(413)

Perform Machine Learning and Advanced Statistical Analysis On Your Own - Even If You Don't Code! 100% in Excel...

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. Azure Machine Learning & MLOps: Beginner to Advance

4.4

(465)

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

18. 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...

19. Hyperparameter Optimization for Machine Learning

4.5

(497)

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more...