Udemy

Machine Learning For Engineering : A-Z

立即報名
  • 91 名學生
  • 更新於 9/2025
  • 可獲發證書
4.2
(18 個評分)
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課程資料

報名日期
全年招生
課程級別
學習模式
修業期
12 小時 3 分鐘
教學語言
英語
授課導師
Dr.Mohammad Samara
證書
  • 可獲發
  • *證書的發放與分配,依課程提供者的政策及安排而定。
評分
4.2
(18 個評分)
10次瀏覽

課程簡介

Machine Learning For Engineering : A-Z

Practical Application of AI in Engineering products

Description

This is a complete course that will prepare you to use Machine Learning in Engineering Applications from A to Z. We will cover the fundamentals of Machine Learning and its applications in Engineering Companies, focusing on 4 types of machine learning: Optimization, Structured data, Reinforcement Learning, and Machine Vision.


What skills will you Learn:

In this course, you will learn the following skills:

  • Understand the math behind Machine Learning Algorithms.

  • Write and build Machine Learning Algorithms from scratch.

  • Preprocess data for Images, Reinforcement learning, structured data, and optimization.

  • Analyze data to extract valuable insights.

  • Use opensource libraries.


We will cover:

  • Fundamentals of Optimization and building optimization algorithms from scratch.

  • Use Google OR Tools optimization library/solver to solve Shop job problems.

  • Fundamentals of Structured Data processing algorithms and building data clustering using K-Nearest Neighbors algorithms from scratch.

  • Use scikit-learn library along with others to predict the Remaining Useful Life of Aircraft Engines (Predictive maintenance).

  • Fundamentals of Reinforcement Learning and building Q-Table algorithms from scratch.

  • Use Keras & Stable baselines libraries to control room temperature and construct a custom-made Environment using OpenAI Gym.

  • Fundamentals of Deep Learning and Networks used in deep learning for machine vision inspection.

  • The use of TensorFlow/ Keras to construct Deep Neural Networks and process images for Classification using CNN (images that have cracks and images that do not) and crack Detection and segmentation using U-Net (outline the crack location in every crack image).

If you do not have prior experience in Machine Learning or Computational Engineering, that's no problem. This course is complete and concise, covering the fundamentals of Machine Learning followed by using real data with strong opensource libraries needed to apply AI in Companies. Let's work together to fulfill the need of companies to apply Machine Learning in Engineering applications to MAKE OUR FUTURE ENGINEERING PRODUCTS SMARTER.

課程章節

  • 9 個章節
  • 52 堂課
  • 第 1 章 Introduction
  • 第 2 章 Optimization - Fundamentals
  • 第 3 章 Optimization - Application
  • 第 4 章 Structured Data - Fundamentals
  • 第 5 章 Structured Data - Application
  • 第 6 章 Reinforcement Learning - Fundamentals
  • 第 7 章 Reinforcement Learning - Application
  • 第 8 章 Computer Vision - Fundamentals
  • 第 9 章 Computer Vision - Application

課程內容

  • Understand the needed AI for Engineering Applications
  • How to Code an Optimize model from scratch
  • How to Code a K-Means Clustering from scratch
  • How to Code a Q table Reinforcement Learning Engine from Scratch
  • Use Google Or-Tools to optimize a plant scheduling problem.
  • Use OpenAI baselines library to solve a control problem.
  • Use Keras to construct a U-net neural network to segment (outline) a crack on a surface.
  • Predict machine failure using real aircraft engine data.


評價

  • M
    Manuel Alejandro Vivas Riverol
    5.0

    I am really enjoying it

  • S
    Stephen Moilanen
    4.5

    This was a good course with material clearly laid out and provided in a logical order. It is definitely worth the price paid. The instructor would code the entire notebook during the lecture which was helpful as he explained what he was doing as he went along, although it could be a bit tedious when he corrected an error or was just typing without saying anything. I came into this course already having a Master degree in Data Science, so most of the technical concepts I was familiar with - not sure how someone less experienced would pick up on the details of the algorithms. I do wish the instructor would provide his slides and offer assignments to assist with learning, rather than just examples. I'm looking forward to Dr. Samara's PINN course, which I also purchased.

  • J
    June Alba
    5.0

    I had a good time learning ML for Engineering.

  • K
    Koritos anagostabilo
    5.0

    Well made course, and a good way for engineers to start using ML in their engineering work.

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