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1.What_if_Adham_Nour_tried_to_make_a_Machine_Learning_Model_at_Home.pptx
This document discusses Adham Nour trying to create a machine learning model at home. It provides an overview of machine learning workflows, including getting data, preparing the data, selecting an algorithm, training the model, and testing the model. It also discusses different machine learning problems like supervised, unsupervised, and reinforcement learning. Key machine learning algorithms are described like support vector machines and how the C parameter impacts regularization. Overall the document serves as an introduction to machine learning concepts for someone trying to build their own model.
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1.What_if_Adham_Nour_tried_to_make_a_Machine_Learning_Model_at_Home.pptx
- 1. What if AdhamNour tried to make a Machine Learning Model, at Home! By. Mohamed Essam
- 2. Whoa! “Computers are ableto see, hear and learn. Welcome to the future.” —~Dave Waters
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- 5. Different between traditionalprogramming and Machine Learning Programming Traditional programming I know the equation Example BMI function is BMI= 𝒘𝒆𝒊𝒈𝒉𝒕 𝒉𝒆𝒊𝒈𝒉𝒕𝟐 Machine Learning Programming I use Function called (train) to get the equation. Example if there is the house cost 600$ and it’s a 200m then the equation will be price= 𝟑𝒙 100 200 300 400 1200 900 600 300 size
- 6. How To Createa Machine Learning model ML Model
- 7. How To Createa Machine Learning model ML Model
- 9. Machine Learning and Starbuckscoffee Workflow
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- 11. “I can’t makebricks without clay”. -Arthur Conan Doyle
- 12. “I can’t makebricks without clay”. -Arthur Conan Doyle
- 14. Data Gathering 1 Might dependon humanwork • Manual labeling for supervisedlearning. • Domain knowledge. Maybe evenexperts. May come for free, or “sortof” • E.g.,Machine Translation. The more the better:Some algorithms need large amounts of data to be useful (e.g.,neural networks). The quantity and quality of data dictate the modelaccuracy
- 15. Machine Learning Workflow Gettingthe Data Preparing data
- 18. Data Preprocessing Is thereanything wrong withthedata? • Missing values • Outliers • Bad encoding (fortext) • Wrongly-labeled examples • Biased data • Do I have manymore samples of one class than therest? Need to fix/removedata? 1
- 19. Machine Learning Workflow Gettingthe Data Preparing data Selecting The Algorithm
- 22. Machine Learning Workflow Gettingthe Data Preparing data Selecting The Algorithm Training the model
- 28. Machine Learning Workflow Gettingthe Data Preparing data Selecting The Algorithm Training the model Testing the model
- 35. Machine Learning workFlow Guidelines • Data is never as you need it • More data is better. • Expect to go backward 3
- 36. Machine Learning workFlow Guidelines • Data is never as you need it • More data is better. • Expect to go backward 3
- 37. Machine Learning isn’tMagic? No, more like Adham Nour Coffee Seeds = Data Coffee Machines= Algorithms Adham Nour= You Cup of Coffee= Model
- 38. Types of MachineLearningProblems Supervised Unsupervised Reinforcement Output is a discrete variable (e.g.,cat/dog) Classification Regression Output is continuous (e.g.,price, temperature)
- 39. Types of MachineLearningProblems Unsupervised There is no desired output. Learn somethingabout the data. Latent relationships. I want to find anomalies in the credit cardusage patterns of my customers. 3 I have photos and want to put them in 20 groups. Supervised Reinforcement
- 40. Types of MachineLearningProblems Supervised Reinforcement Environment gives feedback via a positiveor negative reward signal. An agent interacts with an environment and watches the result of the interaction. Un Supervised
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- 43. Supervised Learning Algorithms Supervised •Linear classifier • Naive Bayes • Support VectorMachines (SVM( • Decision Tree • Random Forests • k-Nearest Neighbors • Neural Networks (Deeplearning) Unsupervised • PCA • t-SNE • k-means • DBSCAN Reinforcement • SARSA–λ • Q-Learning
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- 45. SVM Algorithm Regularization Alsothe ‘ C ‘ parameter in Python’s SkLearn Library Optimises SVM classifier to avoid misclassifying the data. C → large C → small Margin of hyperplane → small Margin of hyperplane → large misclassification(possible) C ---> large , chance of overfit C ---> small , chance of underfitting
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- 47. CREDITS: This presentationtemplate was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik THANKS! Contacts Mhmd96.essam@gmail.com Please keep this slide for attribution