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Python Machine Learning January 2018 - Ho Chi Minh City
This document provides an overview of machine learning concepts using Python. It defines machine learning as the study of how computers can learn without being explicitly programmed. It discusses different machine learning algorithms like supervised vs unsupervised learning, classification vs regression, and clustering vs association. It also gives examples of machine learning applications and recommends Python as a good programming language for machine learning due to useful libraries like NumPy, Pandas, and SciPy. Finally, it outlines future uses of machine learning and provides resources for further learning.
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Python Machine Learning January 2018 - Ho Chi Minh City
- 1. Machine Learning w/Python Andrew Schwabe,10 January 2018 Founder, Saigon A.I. CEO, Formotiv
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- 3. We are hiring! Talk to me after the presentation for more info
- 5. What is MachineLearning? Machine learning is the study of how a computer can learn without being explicitly programmed. - Arthur Samuel 1959 ML helps computers make data-driven decisions Predict results Classify unknown or new data There are MANY algorithms for ML
- 6. A.I. vs. MachineLearning vs. Data Science
- 7. A.I. compared toMachine Learning ML: Self driving car uses ML to create the MOST DIRECT route from A to B A.I. Self driving car sees an accident that slows traffic, and decides to change the route, even though it the distance is further Note: A.I. uses a lot of ML for its data and decisions
- 8. Supervised vs. UnsupervisedLearning Supervised Learning You have a data set with known results, and you “train” your ML model to recognize and predict data that “looks like” your data set Example: ”Learning” from a book or online video Used for: Classification and Regression Unsupervised Learning Your ML model observes new data and makes its own conclusions by looking for patterns Example: Learning a new sport by watching people play Used for: Clustering and Association
- 9. Classification, Regression Classification Is an email message SPAM or not ? What kind of animal is it? What disease does a patient have? (finite number of possible results) Regression What will the stock price be tomorrow ? What will house price be in 6 months? (a predicted numeric value)
- 10. Clustering, Association Clustering Find the different age groups that buy 3-in-1 Café Marketing research Association People who bought 3-in-1 Café usually also buy what products ? Netflix
- 11. Best Programming Languagefor ML ? Universities, research and big companies use PYTHON Java, Lisp, Prolog, and c++ are also popular for developers Data scientists use R, SSAS, Weka and others
- 12. Python! Numpy Libraryfor managing large arrays of data Pandas Library for data structure analysis (wraps numpy) Scipy Library with many tools for maths, science, data analysis and ML These are the packages you need to install to use the sample code
- 13. How does MLactually work? Simple Example 1: Cats vs Dogs Is this good enough to train an ML model? Will predictions be accurate ? Usually you have to convert text like “has fur” to a numeric representation, like has fur = 1, and does not have fur = 0 x1 x2 x3 y 4 legs Has fur Has tail dog 4 legs Has fur Has tail cat 4 legs Has fur Has tail dog
- 14. How does MLactually work? Simple Example 1: Cats vs Dogs More data needed ? More columns needed ? Most important metric: accuracy for prediction Some ML algorithms work better for different data sets x1 x2 x3 x4 x5 y 4 legs Has fur Has tail big bark dog 4 legs Has fur Has tail small meow cat 4 legs Has fur Has tail small bark dog
- 15. How does MLactually work? Simple Example 1: Classification Tree
- 17. Fisher’s Iris DataSet Example Classic data set Only 150 records
- 18. Advanced ML Example Reinforcement Learning Predictions for Bitcoin and other cryptocurrency
- 19. What is ReinforcementLearning?
- 20. Which way toturn? Rewards: +1 -100 +10 +20
- 21. Many Future Usesof ML Food tech with global warming Growing food Fish and farm animals Internet of Things (IoT) Cybersecurity Medical analysis and treatment Smart cars and Robotics Voice control Games
- 22. Good resources https://machinelearningmastery.com/machine-learning-in-python-step-by- step/ https://www.datacamp.com/community/tutorials/pandas-tutorial- dataframe-python https://github.com/rougier/numpy-tutorial https://www.scipy.org/getting-started.html
- 23. Thank you! Followme on: Facebook - fb.me/andrew.schwabe Twitter – aschwabe I will post this presentation and some sample code on Facebook and twitter