Machine learning end to end

Machine learning end to end presentation at GDGBaroda.
Agenda-> Introduction, central tendency, precision recall, validations.

Transcript

1. Krunal Kapadiya @krunal3kapadiya #IndiaMLCC Engineer @
3. - Before we start - What is Machine Learning - Types of Machine Learning - Main challenges - Scikit learn design - Feature scaling - Validating model - Let’s go for it Agenda
4. Before we start
5. - Numpy (Numeric Python) - List [‘Hello’, ‘GDG’, ‘Baroda’] - Dictionaries {‘name’: ‘Krunal’} - Tuples (‘October’, ‘November’) - Pandas (Panel Datas) - Series pd.Series(np.random.randn(5)) - DataFrames pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})) Python libraries
6. Measures of central tendency - Mean - Average values - Median - Middle values - Mode - Mostly repeated value - Standard Deviation - How data is spread in the dataset https://docs.google.com/spreadsheets/d/1KuG47YaIv8xQBEasGeNpNu24dyUT06ADj_0yvK4S67g/edit#gid=0
8. What is Machine Learning?
9. What is Machine Learning? Machine learning is the sub-field of computer science that gives computers the ability to learn without being explicitly programmed - Arthur Samuel, 1959 - Wikipedia Machine learning is about the construction and study of systems that can learn from data. - Freebase
11. Why 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
12. Main challenges 1. Insufficient quantity of training data 2. Non representative training data 3. Poor quality data 4. Irrelevant features 5. Overfitting 6. Underfitting
14. Types of Machine Learning
15. Based on Human supervision 1. Supervised - Labels are known 2. Unsupervised - Labels are not known 3. Semi Supervised - Some labels are available some not 4. Reinforcement Learning - Continuous self learning
16. Based on learning (learn on the fly) 1. Online learning - Learn incrementally 2. Batch learning - Offline learning
17. Based on data patterns 1. Instance based learning - Machine learns by its sample 2. Model based learning - Created model for it
18. Scikit learn design 1. Consistency a. Estimators fit() b. Transformers fit_transform() c. Predictors predict() 2. Inspection imputer.strategy (suffix ‘_’) 3. Nonproliferation of classes Numpy and Scipy sparse matrix 4. Composition pipeline, estimators 5. Sensible defaults comes with default values
19. Feature scaling - Min max Scaling (a.k.a Normalization) Bounds values - Standard Scalar Outliers not much affected To know more about scaling function visit this link
20. Validating model Confusion Matrix - If false negatives are ok, requires high precision, e.g. Spam filter - If false positives are ok, requires high recall, e.g. Medical Diagnosis Precision Recall F-1 Score Accuracy Accuracy = Ratio of correctly classified points / total points
22. GDP Per capita
23. TMDB Dataset Analysis
24. Titanic Dataset Analysis
25. Let’s Go For It 1. Look at the dataset 2. Write down columns and it’s correlation 3. Make questions derived from the dataset 4. Explanatory Analysis with visualization 5. Frame problem 6. Create solution by creating model
27. References and links - https://developers.google.com/machine-learning/crash-course/ml-intro - https://www.kaggle.com/learn/overview - https://www.tensorflow.org/tutorials/ - http://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_sca ling.html - https://pandas.pydata.org/pandas-docs/stable/10min.html - https://www.superdatascience.com/machine-learning/
28. Thank You https://krunal3kapadiya.app/ #IndiaMLCC @krunal3kapadiya if accuracy_score>0.75: