Things that you should know in ml

This is presentation is to understand the basic terms that are used in ML. I covered the most frequently used topics.
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Transcript

1. Krunal Kapadiya Engineer @ @krunal3kapadiya #IndiaMLCC #UFest18 1
3. Input Output Magic What is Machine Learning? 3
4. Types of machine learning Based on Human supervision Supervised ML Unsupervised ML Semi supervised Reinforcement learning 4
5. Types of machine learning Based on learning Online learning Offline learning Based on Data patterns Instance based Model based 5
6. Measures of central tendency What is Mean? - Average value from dataset What is Median? - Middle value of dataset What is Mode? - Repeated value of dataset http://bit.ly/MeasureCentralTendency 6
7. Skewness Remember Negative Skew: Mean is less than mode Positive Skew: Mean is greater than mode 7
8. Data scientist ≠ Data Engineer Machine learning ≠ Data scientist 8
9. Training set and Testing set 9
14. “A” - Auto regression - Time series regression model - Activation Function - Sigmoid - Use in binary classification - ReLU - Helps in hidden layers - Softmax - Mostly used in multiclass classification - A/B Testing - Which technique perform better - Accuracy - correctly predicted values 14
15. When you learn ML in 24 hours 15
16. “B” - Bagging - multiple models and final prediction combining all predictions - Box plot - displays range of variations in data - Backpropagation - update weights reduce errors - Batches - small chunks and splitted data - Batch normalization - improve performance and stability of DNN 16
17. “C” - CNN - Convolutional Neural network - Classification - Labels are known - Cost function - cost function minimum, models accuracy best - Confusion Matrix - displays performance of the model 17
18. “D” - Dropout - Hidden layer dropped to prevent overfitting - Data Augmentation 18
19. “E” - Eager execution - operations runs immediately, waiting for graph execution - Epochs - single training iteration - Early stopping - prevent overfitting “F” - Forward propagation - only one way input to output, no backward 19
20. “G” - Gradient Descendant - Batch GD - Stochastic Gradient Descendant - Mini batch GD 20
21. “G”eneralization (a.k.a out of sample error) - Measure of accuracy for previously unseen data - Difference between expected and proven error - Mostly occurs in deep learning model, training sets working fine, but not fitting in real data 21
22. “H” - Hyperparameters - values set before training model, e.g. batch size, number of tree - Histogram - use to determine skewness “I” - Imputation - wrangling data, filling missing values 22
23. “L” - Learning rate - amount of minimizing in cost function - LSTM - building units in RNN, speech pred, rhythm learning “M” - MLP (Multilevel perception) - aka fully connected layers 23
24. “N” - Numpy - Linspace - Random - Array - Arange “O” - Outliers - value that far away from dataset pattern 24
25. “P” - Pandas - Dataframes - Series - Pooling - use to reduce parameters and prevent overfitting “R” - Regression - predicting values, typically in floating points 25
26. Pooling 26
27. 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 27
28. 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 28
29. Explanatory Analysis - Look at the rows and tables - Find correlated columns - Display it in charts - Give summary based on graph 29
30. TMDB Notebook (Dataset) 30
31. Reference https://www.analyticsvidhya.com/blog/2017/05/25-must-know-terms-concepts-for-begi nners-in-deep-learning/ https://ml-cheatsheet.readthedocs.io 31
33. Thank You 33 https://krunal3kapadiya.app/ @krunal3kapadiya #IndiaMLCC #Ufest18