Delve into practical machine learning with NumPy, pandas, scikit-learn, and more. Gain insights into data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

ml.tar.gz

mnist-fashion

If you're a software engineer looking to add machine learning to your skillset, this is the place to start. 

This course will teach you to write useful code and create impactful machine learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All of these topics are taught using industry-standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Basic knowledge of Python is a prerequisite to this course.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Machine Learning with NumPy, pandas, scikit-learn, and More

<h2 style="font-size:1.1em">Chapter Goals:</h2>
<ul>
  <li>Learn how to save and load <code>Booster</code> models in XGBoost</li>
</ul>

<h2>A. Saving and loading binary data</h2>
<p>After finding the best parameters for a <code>Booster</code> and training it on a dataset, we can save the model into a binary file. Each <code>Booster</code> contains a function called <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.save_model" target="_blank">save_model</a></code>, which saves the model's binary data into an input file.</p>
<p>The code below saves a trained <code>Booster</code> object, <code>bst</code>, into a binary file called <i>model.bin</i>.</p>

# predefined data and labels
dtrain = xgb.DMatrix(data, label=labels)
params = {
  'max_depth': 3,
  'objective':'binary:logistic',
  'eval_metric':'logloss'
}
bst = xgb.train(params, dtrain)

# 2 new data observations
dpred = xgb.DMatrix(new_data)
print('Probabilities:\n{}'.format(
  repr(bst.predict(dpred))))

bst.save_model('model.bin')

<p>We can restore a <code>Booster</code> from a binary file using the <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model" target="_blank">load_model</a></code> function. This requires us to initialize an empty <code>Booster</code> and load the file's data into it.</p>
<p>The code below loads the previously saved <code>Booster</code> from <i>model.bin</i>.</p>

# Load saved Booster
new_bst = xgb.Booster()
new_bst.load_model('model.bin')

# Same dpred from before
print('Probabilities:\n{}'.format(
  repr(new_bst.predict(dpred))))

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to save and load <code>Booster</code> models in XGBoost</li>
</ul>

Save and load Booster objects using XGBoost binary files.

What you'll learn from this course

Data Manipulation with NumPy

Data Analysis with pandas

Data Preprocessing with scikit-learn

Data Modeling with scikit-learn

Clustering with scikit-learn

Gradient Boosting with XGBoost

Deep Learning with TensorFlow

Deep Learning with Keras

Storing Boosters

Chapter Goals:

A. Saving and loading binary data