Explore the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

Become a Machine Learning Engineer

Start your journey toward becoming an ML engineer by learning practical ML skills such as data manipulation, data analysis, data preprocessing, data modeling, clustering, gradient boosting and deep learning. Basic knowledge about Python is a prerequisite. All topics are taught with industry-standard python frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Machine Learning for Software Engineers

## Chapter Goals:
<ul>
 <li>Save and load XGBoost models with the joblib API</li>
</ul>

## A. The joblib API
Since the <code>XGBClassifier</code> and <code>XGBRegressor</code> models follow the same format as scikit-learn models, we can save and load them in the same way. This means using the <a href="https://joblib.readthedocs.io/en/latest/" target="_blank">joblib</a> API.
With the joblib API, we save and load models using the <code><a href="https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html#" target="_blank">dump</a></code> and <code><a href="https://joblib.readthedocs.io/en/latest/generated/joblib.load.html#" target="_blank">load</a></code> functions, respectively. See <a href="https://scikit-learn.org/stable/modules/model_persistence.html" target="_blank">here</a> for specific examples on model persistence with scikit-learn models.

# Chapter Goals:
<ul>
 <li>Save and load XGBoost models with the joblib API</li>
</ul>

# A. The joblib API
Since the <code>XGBClassifier</code> and <code>XGBRegressor</code> models follow the same format as scikit-learn models, we can save and load them in the same way. This means using the <a href="https://joblib.readthedocs.io/en/latest/" target="_blank">joblib</a> API.
With the joblib API, we save and load models using the <code><a href="https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html#" target="_blank">dump</a></code> and <code><a href="https://joblib.readthedocs.io/en/latest/generated/joblib.load.html#" target="_blank">load</a></code> functions, respectively. See <a href="https://scikit-learn.org/stable/modules/model_persistence.html" target="_blank">here</a> for specific examples on model persistence with scikit-learn models.

Save and load XGBoost models using joblib.

Before We Begin

Are You Ready to Become a Machine Learning Engineer?

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

Conclusion

Model Persistence

Chapter Goals:

A. The joblib API