ML at Udaipur

Machine Learning introduction and glossaries at GDG Udaipur

Transcript

1. Machine Learning at Udaipur Krunal Kapadiya @krunal3kapadiya
2. Agenda - Introduction to Machine Learning - Steps to towards into ML - Problems in Machine Learning Data - Ending note
4. Introduction to Machine Learning
5. What is Machine Learning Input Output It’s not Dr Strange, It’s magic tricks
7. A. Classification B. Regression C. Dimensity Reduction D. Clustering Q. I want to predict values I will use
8. A. Classification B. Regression C. Dimensity Reduction D. Clustering Q. I want to predict values I will use
9. What is Machine Learning "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 For example: I need a program that will tell me which tweets will get retweets. - Task (T): Classify a tweet that has not been published as going to get retweets or not. - Experience (E): A corpus of tweets for an account where some have retweets and some do not. - Performance (P): Classification accuracy, the number of tweets predicted correctly out of all tweets considered as a percentage.
10. What is Machine Learning - Data used - 2005 - 130 Exabytes - 2010 - 1200 Exabytes - 2015 - 7900 Exabytes - 2020 - 40900 Exabytes 1 EB = 1000 PB = 1 million TB = 1 billion GB 1 billion GB = 10 Thousand Crore TB
11. Applications of Machine Learning
12. Applications of Machine Learning - In computer vision - In data predictor, stock market predictor - In data segmentations (customer segmentation) - Anomaly detection - Sentiment analysis
14. Types of Data
15. Types of Data : Structured Data
16. Types of Data: Unstructured Data
17. Machine Learning != Data Science != Data Analyst
18. Machine Learning != Data Science != Data Analyst
19. Steps in ML
20. Set Goal Split Data Train Model Results Test Model Steps in ML
21. Steps in ML Training and splitting data with validations 20% Test case 80% Training set Total numbers of training set
22. Steps in ML Training and splitting data with validations 15% Test set 70% Training set Total numbers of training set 15% Validation
23. Set Goal Split Data Train Model Results Test Model Steps in ML
24. Source: https://www.superdatascience.com/machine-learning/ Problem: Find whether user will purchased or not
25. Set Goal Split Data Train Model Results Test Model Steps in ML
26. Set Goal Split Data Train Model Results Test Model Steps in ML
27. Steps in ML Python from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split Import pandas as pd pd.read_csv(‘Social_Network_Ads.csv’) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0 R library(caTools) dataset = read.csv('Social_Network_Ads.csv') split = sample.split(dataset$Purchased, SplitRatio = 0.75
28. A. cross_validation B. model_selection C. crossValidation D. modelSelection Q. To train and split data we are using now
29. A. cross_validation B. model_selection C. crossValidation D. modelSelection Q. To train and split data we are using now
30. Set Goal Split Data Train Model Results Test Model Steps in ML
31. Steps in ML Python from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) classifier.fit(X_train, y_train) R training_set[-3] = scale(training_set[-3]) test_set[-3] = scale(test_set[-3]) library(rpart) classifier = rpart(formula = Purchased ~., data = training_set)
32. Q. Standard scaler can have value more than one? A. Yes B. No
33. Q. Standard scaler can have value more than one? A. Yes B. No
34. Set Goal Split Data Train Model Results Test Model Steps in ML
35. Steps in ML Python from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) pickle.dumps(classifier, open(‘classifier_model_in_python.pkl’, ‘wb’)) R # Making the Confusion Matrix cm = table(test_set[, 3], y_pred) saveRDS(final_model, "classifier_model_in_R.rds")
36. Set Goal Split Data Train Model Results Test Model Steps in ML
37. Results
38. A. X B. Y Independent Variable is generally displayed in following axis
39. A. X B. Y Independent Variable is generally displayed in following axis
40. Results
41. Confusion Matrix Actual True Actual False Predicted True Predicted False False Negative False Positive
42. Another Problem Statement Given a Student’s High School GPA, predict his University GPA
43. Find apple or orange problem Traditional approach to solve problem
44. Weight Texture Label 150g Bumpy Orange 170g Bumpy Orange 140g Smooth Apple 130g Smooth Apple Feature Feature Find apple or orange problem Training Data
45. Weight Texture Label 150g Bumpy Orange 170g Bumpy Orange 140g Smooth Apple 130g Smooth Apple Feature Feature Examples Find apple or orange problem Training Data
46. Orange Apple Weight = 150 G Yes No Yes No Texture = bumpy ? ... Decision Tree Find apple or orange problem
47. An Algorithm to Identify Numbers
48. An Algorithm to Identify Numbers
49. An Algorithm to Identify Numbers
50. An Algorithm to Identify Numbers
51. Google IO 2019 Coral Source: https://www.youtube.com/watch?v=Jgm25QdF90A
52. Problems in Machine Learning Data
53. But, real problems are... • Insufficient quantity of training data • Non representative training data • Poor quality data • Irrelevant features • Overfitting • Underfitting
54. A. Insufficient Data B. Poor quality of data C. Overfitting D. Underfitting Q. Challenges in ML
55. A. Insufficient Data B. Poor quality of data C. Overfitting D. Underfitting Q. Challenges in ML
56. Ending note
57. How can I start it • Look at the dataset • Write down columns and it’s correlation • Make questions derived from the dataset • Explanatory Analysis with visualization • Frame problem • Create solution by creating model
58. Start learning by own • https://developers.google.com/machine-learning/crash-course/ml-intro • https://www.kaggle.com/learn/overview • https://www.tensorflow.org/tutorials/ • https://pandas.pydata.org/pandas-docs/stable/10min.html
59. That’s it
60. Thank You @krunal3kapadiya https://krunal3kapadiya.app