Machine Learning Training for Industrial Applications

Detailní popis produktu

Course Goal:
The course focuses on applying machine learning in manufacturing processes. Students will learn the basic techniques and tools for analyzing and optimizing production using machine learning, particularly using Python libraries such as Scikit-learn, Numpy, Pandas, and data visualization tools.

Requirements:

• Knowledge of Python (at least basic level)

• Basic understanding of analytics and statistics

Module 1: Introduction to Machine Learning and Its Applications in Manufacturing

• What is Machine Learning?

  • Definition of machine learning

  • Types of machine learning (supervised, unsupervised, reinforcement learning)

• Applications in Manufacturing:

  • Yield prediction

  • Fault prediction

  • Production process optimization

  • Predictive maintenance

• Basics of Data Handling:

  • Data import (CSV, Excel, databases)

  • Data cleaning and preprocessing

  • Handling missing values

Module 2: Basics of ML Libraries (Scikit-learn, Numpy, Pandas)

• Numpy and Pandas for efficient data handling:

  • Working with Numpy arrays (numpy.array)

  • Pandas DataFrames and Series

  • Filtering, aggregation, and data manipulation

  • Data cleaning, handling missing values, and normalization

• Data Visualization:

  • Matplotlib and Seaborn for visualization

  • Creating basic charts (histograms, boxplots, scatter plots)

  • Visualizing correlations and trends in production data

Module 3: Machine Learning Models for Manufacturing Data

• Regression Models:

  • Linear regression

  • Multiple linear regression

  • Model evaluation (MSE, RMSE, R²)

• Classification Models:

  • Logistic regression

  • Decision trees

  • Random Forest and SVM

  • Evaluation metrics (accuracy, recall, F1-score)

• Anomaly Detection:

  • K-means clustering for anomaly detection

  • Algorithms for detecting anomalies in production processes

• Preprocessing and Feature Engineering:

  • Selection and transformation of input variables

  • Standardization and normalization

  • Cross-validation

Module 4: Predictive Models for Production Optimization

• Yield and Quality Prediction:

  • Using regression models to predict product quality

  • Implementing linear and non-linear models for yield analysis

  • Modeling defective and inefficient processes

• Predictive Maintenance:

  • Models for predicting equipment failures (SVM, decision trees, etc.)

  • Implementing models for condition monitoring and fault prediction

• Production Parameter Optimization:

  • Applying ML to optimize process parameters

  • Evolutionary algorithms and optimization techniques for production improvement

Module 5: Advanced Machine Learning and Deep Learning

• Deep Learning in Manufacturing:

  • Introduction to deep neural networks

  • Using deep learning for complex production data analysis

  • CNN and RNN applications for prediction and sequence analysis

• Reinforcement Learning for Process Optimization:

  • Introduction to reinforcement learning

  • Modeling and applying RL algorithms to improve production decisions

Module 6: Case Studies and Practical Applications

• Case Study 1: Real-time Machine Fault Prediction

  • Data collection on equipment status

  • Building a fault prediction model

  • Model implementation and testing effectiveness

• Case Study 2: Production Process Optimization

  • Identifying key parameters for optimization

  • Choosing the appropriate model

  • Evaluating and implementing results

Module 7: Final Project

• Individual Project:
  Students create a project involving data collection, analysis, and production prediction using ML. Projects focus on a specific problem in the production process and require applying ML models.

• Presentation of Results:
  Students present their projects and analysis outcomes.

Recommended Tools and Libraries:

• Python 3.x

• Scikit-learn

• Pandas

• Numpy

• Matplotlib

• Seaborn

Expected Outcomes:

• Ability to apply basic ML models to manufacturing data

• Skills in analyzing, predicting, and optimizing production processes

• Experience with data visualization tools and result interpretation