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 about the purpose of cross-validation</li>
  <li>Implement a function that applies the K-Fold cross-validation algorithm to a model</li>
</ul>

<h2> Chapter Goals: </h2>
<ul>
  <li>Learn about the purpose of cross-validation</li>
  <li>Implement a function that applies the K-Fold cross-validation algorithm to a model</li>
</ul>

Learn about K-Fold cross-validation and why it's used.

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

Cross-Validation

Chapter Goals: