Machine Learning For Engineering : A-Z
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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 - Fundamentals
Section 2 Files
Lecture 6: General Optimization Techniques
Lecture 7: Greedy Randomized Adaptive Search Procedure (GRASP)
Lecture 8: Imports, Data input
Lecture 9: Cost, Seed Functions
Lecture 10: Ranking Function
Lecture 11: Local Search
Lecture 12: PartA: Restricted Candidate List (RCL)
Lecture 13: PartB: Restricted Candidate List (RCL)
Lecture 14: Main Iteration
Section 3: Optimization - Application
Section-3 Files
Lecture 15: Job Shop Problem
Lecture 16: Integer Linear Programming
Lecture 17: set needed data
Lecture 18: set variables
Lecture 19: set constraints
Lecture 20: set objective
Lecture 21: solve results
Section 4: Structured Data - Fundamentals
Section-4 Files
Lecture 22: Supervised and Unsupervised Machine Learning
Lecture 23: K-means Clustering
Lecture 24: import libraries
Lecture 25: Data Preprocessing
Lecture 26: Calculate Distance
Lecture 27: Centroid Initialization
Lecture 28: Main Loop
Lecture 29: Results Assessment
Section 5: Structured Data - Application
Section-5 Files
Lecture 30: Download the Data
Lecture 31: Understand the General Data
Lecture 32: Data Exploration
Lecture 33: Data Arrangement
Lecture 34: Data Preparation
Lecture 35: KNN (K-Nearest Neighbors)
Lecture 36: Support Vector Machine (SVM)
Lecture 37 : Random Forest
Section 6: Reinforcement Learning - Fundamentals
Section-6 Files
Lecture 38: Reinforcement Learning Fundamentals
Lecture 39 : Environment
Lecture 40: Settings
Lecture 41: Main Loop
Section 7: Reinforcement Learning - Application
Section-7 Files
Lecture 42: Coding RL Deep Q Learning
Lecture 43: Coding RL using OpenAI-Baselines
Section 8: Computer Vision - Fundamentals
section8.zip
Lecture 44:Deep Learning
Lecture 45:Convolutional Neural Network (CNN)
Lecture 46: Data Preprocessing
Lecture 47: Build/Training the model
Lecture 48: Results
Section 9: Computer Vision - Application
section_9.zip
Lecture 49: Data Preprocessing_Part1
Lecture 50: Data Preprocessing_Part2
Lecture 51: Training
Lecture 52: Results
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Course
Machine Learning For Engineering : A-Z
Machine Learning For Engineering : A-Z
Machine Learning For Engineering : A-Z
Buy now
Learn more
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 - Fundamentals
Section 2 Files
Lecture 6: General Optimization Techniques
Lecture 7: Greedy Randomized Adaptive Search Procedure (GRASP)
Lecture 8: Imports, Data input
Lecture 9: Cost, Seed Functions
Lecture 10: Ranking Function
Lecture 11: Local Search
Lecture 12: PartA: Restricted Candidate List (RCL)
Lecture 13: PartB: Restricted Candidate List (RCL)
Lecture 14: Main Iteration
Section 3: Optimization - Application
Section-3 Files
Lecture 15: Job Shop Problem
Lecture 16: Integer Linear Programming
Lecture 17: set needed data
Lecture 18: set variables
Lecture 19: set constraints
Lecture 20: set objective
Lecture 21: solve results
Section 4: Structured Data - Fundamentals
Section-4 Files
Lecture 22: Supervised and Unsupervised Machine Learning
Lecture 23: K-means Clustering
Lecture 24: import libraries
Lecture 25: Data Preprocessing
Lecture 26: Calculate Distance
Lecture 27: Centroid Initialization
Lecture 28: Main Loop
Lecture 29: Results Assessment
Section 5: Structured Data - Application
Section-5 Files
Lecture 30: Download the Data
Lecture 31: Understand the General Data
Lecture 32: Data Exploration
Lecture 33: Data Arrangement
Lecture 34: Data Preparation
Lecture 35: KNN (K-Nearest Neighbors)
Lecture 36: Support Vector Machine (SVM)
Lecture 37 : Random Forest
Section 6: Reinforcement Learning - Fundamentals
Section-6 Files
Lecture 38: Reinforcement Learning Fundamentals
Lecture 39 : Environment
Lecture 40: Settings
Lecture 41: Main Loop
Section 7: Reinforcement Learning - Application
Section-7 Files
Lecture 42: Coding RL Deep Q Learning
Lecture 43: Coding RL using OpenAI-Baselines
Section 8: Computer Vision - Fundamentals
section8.zip
Lecture 44:Deep Learning
Lecture 45:Convolutional Neural Network (CNN)
Lecture 46: Data Preprocessing
Lecture 47: Build/Training the model
Lecture 48: Results
Section 9: Computer Vision - Application
section_9.zip
Lecture 49: Data Preprocessing_Part1
Lecture 50: Data Preprocessing_Part2
Lecture 51: Training
Lecture 52: Results
Learn more
Buy now
Section 1: Introduction
5 Lessons
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 - Fundamentals
10 Lessons
Section 2 Files
Lecture 6: General Optimization Techniques
Lecture 7: Greedy Randomized Adaptive Search Procedure (GRASP)
Lecture 8: Imports, Data input
Lecture 9: Cost, Seed Functions
Lecture 10: Ranking Function
Lecture 11: Local Search
Lecture 12: PartA: Restricted Candidate List (RCL)
Lecture 13: PartB: Restricted Candidate List (RCL)
Lecture 14: Main Iteration
Section 3: Optimization - Application
8 Lessons
Section-3 Files
Lecture 15: Job Shop Problem
Lecture 16: Integer Linear Programming
Lecture 17: set needed data
Lecture 18: set variables
Lecture 19: set constraints
Lecture 20: set objective
Lecture 21: solve results
Section 4: Structured Data - Fundamentals
9 Lessons
Section-4 Files
Lecture 22: Supervised and Unsupervised Machine Learning
Lecture 23: K-means Clustering
Lecture 24: import libraries
Lecture 25: Data Preprocessing
Lecture 26: Calculate Distance
Lecture 27: Centroid Initialization
Lecture 28: Main Loop
Lecture 29: Results Assessment
Section 5: Structured Data - Application
9 Lessons
Section-5 Files
Lecture 30: Download the Data
Lecture 31: Understand the General Data
Lecture 32: Data Exploration
Lecture 33: Data Arrangement
Lecture 34: Data Preparation
Lecture 35: KNN (K-Nearest Neighbors)
Lecture 36: Support Vector Machine (SVM)
Lecture 37 : Random Forest
Section 6: Reinforcement Learning - Fundamentals
5 Lessons
Section-6 Files
Lecture 38: Reinforcement Learning Fundamentals
Lecture 39 : Environment
Lecture 40: Settings
Lecture 41: Main Loop
Section 7: Reinforcement Learning - Application
3 Lessons
Section-7 Files
Lecture 42: Coding RL Deep Q Learning
Lecture 43: Coding RL using OpenAI-Baselines
Section 8: Computer Vision - Fundamentals
6 Lessons
section8.zip
Lecture 44:Deep Learning
Lecture 45:Convolutional Neural Network (CNN)
Lecture 46: Data Preprocessing
Lecture 47: Build/Training the model
Lecture 48: Results
Section 9: Computer Vision - Application
5 Lessons
section_9.zip
Lecture 49: Data Preprocessing_Part1
Lecture 50: Data Preprocessing_Part2
Lecture 51: Training
Lecture 52: Results