Courses

Introduction to Machine Learning for Engineers

Introduction to Machine Learning for Engineers
300 USD
Programming For Engineers
9 Courses
244 Lessons in 42 Chapters
25 Hours & 24 Minutes
  Diploma Info   Buy Now
Lessons
29 Lessons in 5 Chapters
Duration
3 Hours & 43 Minutes
Students
0 Student in 0 Country
Instructor
Mostafa Emad Engineering Software Developer
By the end of this module, participants will understand the fundamental concepts of machine learning, including supervised and unsupervised learning techniques. They will be able to implement machine learning models using Python, preprocess and analyze engineering data, and apply algorithms for classification, regression, and clustering. Additionally, they will recognize machine learning’s role in engineering applications and its potential for solving Simple problems.
Course Curriculum
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Chapter 01 - Introduction to AI and Machine Learning 4 Lessons
 0:31:00
01-01 - Introduction to Artificial Intelligence
 0:05:00
01-02 - What is Machine Learning
 0:08:00
01-03 - Machine Learning Application for Engineering
 0:09:00
01-04 - Types of Machine Learning , Supervised , Unsupervised , Reinforcement Learning
 0:09:00
Chapter 02 - Data Processing and Cleaning 6 Lessons
 0:42:00
02-01 - Sources of engineering data (sensors, logs, simulations, real-world measurements)
 0:06:00
02-02 - Structured vs. unstructured data
 0:08:00
02-03 - Working with Engineering Datasets
 0:08:00
02-04 - Handling missing values and duplicates
 0:06:00
02-05 - Data Normalization and Standardization
 0:06:00
02-06 - Encoding Categorical Variables for ML models
 0:08:00
Chapter 03 - Supervised Learning for Engineers 9 Lessons
 1:10:00
03-01 - What is Regression,
 0:06:00
03-02 - Linear regression for predictive Analytics
 0:08:00
03-03 - Polynomial Regression
 0:06:00
03-04 - Classification Techniques for Engineering Problems
 0:06:00
03-05 - Logistic Regression
 0:08:00
03-06 - Decision Trees and Random Forests
 0:12:00
03-07 - Evaluating Supervised Learning Models : Performance metrics: MSE, RMSE, R² for regression
 0:10:00
03-08 - Evaluating Supervised Learning Models : Accuracy, precision, recall, and F1-score for classification
 0:08:00
03-09 - Evaluating Supervised Learning Models : Overfitting and Underfitting
 0:06:00
Chapter 04 - Unsupervised Learning and Clustering4 Lessons
 0:29:00
04-01 - What is unsupervised learning?
 0:06:00
04-02 - Applications of clustering in engineering (fault detection, material classification)
 0:09:00
04-03 - Understanding K-Means clustering algorithm
 0:06:00
04-04 - Hierarchical Clustering for Engineering Datasets
 0:08:00
Chapter 05 - Neural Networks and Deep Learning Basics6 Lessons
 0:51:00
05-01 - What is Neural Network
 0:08:00
05-02 - How do neurons and layers work
 0:09:00
05-03 - Applications of Deep Learning in Engineering
 0:08:00
05-04 - Setting up a basic neural network
 0:10:00
05-05 - Training and Evaluation
 0:08:00
05-06 - Perform Prediction with ANN
 0:08:00

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