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

<h2 style="font-size:1.1em">Chapter Goals:</h2>
<ul>
  <li>Learn how to create a scikit-learn style regression model in XGBoost</li>
</ul>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to create a scikit-learn style regression model in XGBoost</li>
</ul>

<h2>A. XGBoost linear regression</h2>
<p>In addition to scikit-learn style classifiers, XGBoost also provides a scikit-learn style linear regression model with the <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor" target="_blank">XGBRegressor</a></code> object.</p>

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

XGBoost Regressor

Chapter Goals: