Ai For Engineering Applications: A-Z

AI For Engineering Applications: A-Z

What you'll learn

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.

Requirements

High School Maths

Basic Python knowledge

Description

DescriptionThis 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 to apply Machine Learning to the different types of machine learning.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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure

Lecture 3 Common AI Applications in Engineering Companies

Lecture 4 Course Requirements

Lecture 5 Installing Anaconda

Section 2: Optimization

Lecture 6 General Optimization Techniques

Lecture 7 Greedy Randomized Adaptive Search Procedure (GRASP)

Lecture 8 GRASP Coding_1: Imports, Data input

Lecture 9 GRASP Coding_2: Cost, Seed Functions

Lecture 10 GRASP Coding_3: Ranking Function

Lecture 11 GRASP Coding_4: Local Search

Lecture 12 GRASP Coding_5_PartA: Restricted Candidate List (RCL)

Lecture 13 GRASP Coding_5_PartB: Restricted Candidate List (RCL)

Lecture 14 GRASP Coding_6: Main Iteration

Lecture 15 Job Shop Problem

Lecture 16 Integer Linear Programming

Lecture 17 Job Shop Cooding_1_set_needed_data

Lecture 18 Job Shop Cooding_2_set_variables

Lecture 19 Job Shop Cooding_3_set_constraints

Lecture 20 Job Shop Cooding_4_set_objective

Lecture 21 Job Shop Cooding_5_solve_results

Section 3: Structured Data

Lecture 22 Supervised and Unsupervised Machine Learning

Lecture 23 K-means Clustering

Lecture 24 K-means Clustering Coding_1_import_libraries

Lecture 25 K-means Clustering Coding_2_Data_Preprocessing

Lecture 26 K-means Clustering Coding_3_Calculate_Distance

Lecture 27 K-means Clustering Coding_4_Centroid_Initialization

Lecture 28 K-means Clustering Coding_5_Main_Loop

Lecture 29 K-means Clustering Coding_6_Results_Assessment

Lecture 30 Predictive Maintenance 1: Download the Data

Lecture 31 Predictive Maintenance 2: Understand the General Data

Lecture 32 Predictive Maintenance 3: Data Exploration

Lecture 33 Predictive Maintenance 4: Data Arrangement

Lecture 34 Predictive Maintenance 5: Data Preparation

Lecture 35 Predictive Maintenance 6: KNN (K-Nearest Neighbors)

Lecture 36 Predictive Maintenance 7: Support Vector Machine (SVM)

Lecture 37 Predictive Maintenance 8: Random Forest

Section 4: Reinforcement Learning

Lecture 38 Reinforcement Learning Fundamentals

Lecture 39 Coding Q_Table: Environment

Lecture 40 Coding Q_Table: Settings

Lecture 41 Coding Q_Table: Main Loop

Lecture 42 Coding Deep Q Learning

Lecture 43 Coding using Openai-Baselines

Section 5: Machine Vision

Lecture 44 Deep Learning

Lecture 45 Convolutional Neural Network (CNN)

Lecture 46 Coding CNN: Data Preprocessing

Lecture 47 Coding CNN: Build/Training the model

Lecture 48 Coding CNN: Results

Lecture 49 Coding U_NET: Data Preprocessing_Part1

Lecture 50 Coding U_NET: Data Preprocessing_Part2

Lecture 51 Coding U_NET: Training

Lecture 52 Coding U_NET: Results

Engineers and Programmers whom want to get familiar with applying AI for Engineering applications