1. Python Machine Learning
With
Jupyter Notebook
Speakers
Thong Nguyen
Truoc Pham
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2. Target & Audiences
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3. Agenda
What is Machine Learning?
How to get started?
Our suggestion
Simple Machine Learning project
Q&A
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4. What is Machine Learning?
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6. YouTube
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7. 7
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8. Machine Learning is
using data to answer questions
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9. How to get started?
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10. 10
Languages
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12. Our suggestion
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13. Python
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# Python 3: Fibonacci series up to n
>>> def fib(n):
>>> a, b = 0, 1
>>> while a < n:
>>> print(a, end=' ')
>>> a, b = b, a+b
>>> print()
>>> fib(1000)
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
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14. 18K users
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83% using Python
https://www.kaggle.com/kaggle/kaggle-survey-2018/discussion/74297
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15. Libs for ML
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16. Jupyter Notebook
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17. 17https://www.kaggle.com/kaggle/kaggle-survey-2018/discussion/74297
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19. 19
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20. 20
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21. Simple Machine Learning project
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22. 22
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23. Demo
https://devdayml2019.herokuapp.com/
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24. 24
Data → Processing data → Training → Model → Serve Predictions
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25. 25
Preparing Data → Processing Data → Training → Model → Serve Predictions
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Preparing Data → Processing Data → Training → Model → Serve Predictions
Data for demo
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
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27. Preparing Data → Processing Data → Training → Model → Serve Predictions
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- 1460 rows, 81 columns
- Data types include:
int64
float64
object
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28. Preparing Data → Processing Data → Training → Model → Serve Predictions
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- Fill NaN
- Outlier
- Data Transformation
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29. Preparing Data → Processing Data → Training → Model → Serve Predictions
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train = train.replace([-np.inf, np.inf], 0.0)
train = train.fillna(0.0)
test = test.replace([-np.inf, np.inf], 0.0)
test = test.fillna(0.0)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for filling NaN by Zero
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31. Preparing Data → Processing Data → Training → Model → Serve Predictions
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32. 32
train.drop(
train[(train['GrLivArea'] > 4000)].index,
inplace=True
)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for removing outliers
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33. Preparing Data → Processing Data → Training → Model → Serve Predictions
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def label_encoding(df):
cat_features = df.select_dtypes(include=['object']).columns
lbl = LabelEncoder()
for col in cat_features:
df[col] = lbl.fit_transform(list(df[col].values.astype('str')))
return df
train = label_encoding(train)
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for label encoding
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35. Preparing Data → Processing Data → Training → Model → Serve Predictions
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Linear regression
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ignore_cols = ['Id', 'SalePrice']
train_features = [col for col in train.select_dtypes(include=['float64',
'int64']).columns
if col not in ignore_cols]
X = train[train_features].copy()
y = np.log1p(train['SalePrice'])
clf = LinearRegression()
clf.fit(X, y)
Using simple Linear Regression model
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Preparing Data → Processing Data → Training → Model → Serve Predictions

37. Preparing Data → Processing Data → Training → Model → Serve Predictions
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Evaluation Model
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def rmse_score(y_obs, y_hat):
rmse = np.sqrt(mean_squared_error(y_obs, y_hat))
return rmse
X_trn, X_val, y_trn, y_val = train_test_split(
X, y, test_size=0.2, random_state=42)
clf.fit(X_trn, y_trn)
y_hat = clf.predict(X_val)
rmse = rmse_score(y_val, y_hat)
print('RMSE score: {:.6f}'.format(rmse))
Preparing Data → Processing Data → Training → Model → Serve Predictions
Sample code for Evaluation Model
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39. Prepare Data → Processing Data → Training → Model → Serve Predictions
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What is my house price?
New Info ML Web Service Predicted Price
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40. Let’s getting started your wonderful Machine Learning project with
Python & Jupyter Notebook
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41. References
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http://bit.ly/agilityio-ml-2019
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42. Thank You
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Speakers
Thong Nguyen - nguyenthonght@gmail.com
Truoc Pham - khactruoc09dce@gmail.com

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