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Python and Machine Learning Applications in Industry


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During Artificial Intelligence in Industry 4.0 Workshop, keynote speaker Michał Krasoń, a Data Scientist from Stermedia, shared his knowledge about Machine Learning from basic definitions to advanced case studies such as nitrous oxide emissions in turbines and anomaly detection from video streams.

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Python and Machine Learning Applications in Industry

  1. 1. Python and Machine Learning Applications in Industry Michał Krasoń Data Scientist AI Engineer
  2. 2. INTELLIGENT FACTORIES AI DRIVEN FACTORIES By leveraging data, sensors, and robots, U. S. manufacturing output is 40% higher that two decades ago.Due to the use of data, sensors and robots, industrial production is 40% higher than 20 years ago By incorporating advanced IoT technologies and AI, factories can generate trillions trillions by 2025 Inefficiencies and waste result in billions of dollars lost every year 60 -70% of wasted materials 40% higher output $3.7 trillion in new value AI changes the production TRADITIONAL FACTORIES
  3. 3. why ML and AI is important now? Key factors: • Hardware • Algorithms • Data • Networking • Cloud
  4. 4. What is machine learning? Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Deals with processes in which the computer solves tasks in a way that imitates human behavior. Artificial intelligence (AI) Machine learning (ML) Algorithms that allow computers to learn from examples without being explicitly programmed Deep learning (DL) An ML subset that uses deep neural networks as models
  5. 5. ML vs non-ML ‘Traditional’ programming Machine learning Output Historical output New data Historical data Data Program (model) Program Output
  6. 6. machine learning: applications in industry
  7. 7. ML vs nie - MLML : applications in industry Task: Predicting the wear of diamond drill tips
  8. 8. ML : applications in industry Task: Reduction of nitrous oxide emissions from turbines
  9. 9. ML : applications in industry Task: Automatic fault detection - quality control on the line
  10. 10. ML : applications in industry Task: Anomaly detection on industrial cameras
  11. 11. ML : usage Task: Selection of the best candidates for the position
  12. 12. machine learning: types of algorithms
  13. 13. types of ML algorithms Supervised Unsupervised Reinforcement learning
  14. 14. supervised learning 1. Regression: a. Linear regression b. Gradient boosted decision tree (GBDT) 2. Classification: a. K nearest neighbours b. SVM
  15. 15. unsupervised learning 1. Clustering 2. Dimensionality reduction
  16. 16. deep learning 1. A lot of complex data (texts, images) 2. Neural networks a. "Classic" deep networks b. Convolutional - image recognition (CV) c. Recurrent - Natural Language Processing (NLP)
  17. 17. why Python? Less code to achieve results A lot of ML-dedicated libraries Readable, structured code Easy prototyping Production - ready
  18. 18. case study: demand prediction
  19. 19. cs: prediction of demand Situation: production hall, we want to predict the use of additional parts on individual days Data the need for elements and consumption of additional parts in the last 4 months, the need for elements in the future Problem: few dates, many elements affecting consumption
  20. 20. krok 1: wybór klasy modeli, train- test split 1. Too little data to use advanced ML algorithms 2. Dependence is intuitively linear - we use linear models The teaching part The test part
  21. 21. step 2: limiting the number of variables Too many elements make modeling difficult. We do feature selection: 1. Study of correlation with the target variable 2. Dimensionality reduction, grouping of elements 3. Use of automatic methods of eliminating features
  22. 22. step 3: choice of model, prediction 1. We adjust models to learning data, we choose the best based on cross validation 2. We perform the prediction on the test set
  23. 23. Michał Krasoń Data Scientist e-mail: Linkedin: Thank you :)