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 multiclass classification</li>
  <li>Understand the purpose of multiple hidden layers</li>
  <li>Learn the pros and cons of adding hidden layers</li>
</ul>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about multiclass classification</li>
  <li>Understand the purpose of multiple hidden layers</li>
  <li>Learn the pros and cons of adding hidden layers</li>
</ul>

Understand the differences between binary and multiclass classification.

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

Multiclass

Chapter Goals: