Intro to machine learning
- 1. Intro to Machine Learning Andreas Chandra
- 2. Structure ● Intro to Machine Learning ● Practice Machine Learning Python | R ● Real Case 1 (Regression) ● Real Case 2 (Classification) Note that we don’t discuss technical things
- 3. Concept Practical machine learning with python, Sarkar et al
- 4. Concept ● Field of study that gives computers the ability to learn without being explicitly programmed - Arthur Samuel ● A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. -Tom M. Mitchell.
- 5. Flow
- 6. Tabular, Text, Image, Speech, What’s Next?
- 7. Supervised Learning Concept: take in data samples and associated outputs with each data sample during the training process Objective: to learn a mapping or association between input data samples x and their corresponding output y’ based on multiple training data instances. Algorithms: K-NN, SVM, Naive Bayes, Decision Tree (Random Fores & XGBoost)
- 8. Unsupervised Learning Concept: the model don’t need the labels but tries to learn inherent latent structures, patterns and relationship from given data without any supervision. Algorithms: K-Means, K-Median, DBSCAN, Association rule
- 9. Evaluation ● Cross Validation ● Dummy Classifier ● Accuracy, Precission, Recall, F1-Score
- 10. Exercise - Concept Suppose your program watches which person classified you do or you do not mark as verified, and base on that learns how to better filter verified. What is the experience, task, and performance in this condition?
- 11. Exercise - Evaluation What situation should we consider for precission and recall? https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c
- 12. Exercise - Use Cases ● E-Commerce ● Travel ● News & Media ● Entertainment ● Finance