The 7 steps of machine learning are: 1) gathering data, 2) data preparation, 3) choosing a model, 4) training the model, 5) evaluating the model, 6) tuning hyperparameters, and 7) using the trained model to make predictions on new data. Each step is important, as the quality of predictions depends on the quality and quantity of data collected and how well the model is trained, evaluated, and tuned. The goal is to end with a model that can accurately predict outcomes for unseen data.

1. The 7 Steps of Machine Learning
Waziri Shebogholo
July 19, 2019
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2. The 7 Steps of Machine Learning
1 Gathering data
2 Data preparation
3 Choosing a model
4 Training
5 Evaluation
6 Hyperparameter tuning
7 Prediction
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3. 1. Gathering data
How good our predictive model will be, will depend on:-
1 Quantity of data
2 Quality of data
3 Learning algorithm
Machine Learning model is only as good as the data it is built from.
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4. 2. Data preparation
1 Exploration data analysis (EDA)
2 Dealing with missing values
3 Dealing with outliers
4 Dealing with imbalanced data
5 Data transformation
6 Feature engineering
7 Split data into training and validation sets and set aside some
data for testing your model.
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5. 3. Choosing a model
Guidelines for choosing a Learning Algorithm
1 Type of the problem (eg. Classification, Regression, Clustering)
2 Nature, type and size of data
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6. 4. Training
The goal of training/learning is to find a model and its corresponding
parameters such that the resulting model perform well on unseen
data.
What might happen?
Underfitting
Overfitting
Generalization
What might help?
1 Feature selection
2 Regularization
3 Model architecture
4 More data
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7. 5. Evaluation
Classification
1 Classification accuracy
2 Confusion matrix
3 Logarithmic loss (log-loss)
4 Precision and recall
5 F-Score
6 Area under curve (AUC)
Regression
1 Mean absolute error
2 Mean squared error
3 Root mean squared error
4 Mean squared log error
5 Median absolute error
6 Regression score (r2
score)
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8. 6. Hyperparameter tuning
Experimental process
Art rather than science
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9. 7. Prediction
What do we have to this point?
1 Trained model
2 The right model parameters
Use trained model to predict unseen data point(s).
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10. THANK YOU.
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