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>Convert logits to probabilities</li>
  <li>Obtain model predictions from probabilities</li>
  <li>Calculate prediction accuracy</li>
</ul>

<h2>Chapter Goals:</h2>
<ul>
  <li>Convert logits to probabilities</li>
  <li>Obtain model predictions from probabilities</li>
  <li>Calculate prediction accuracy</li>
</ul>

Discover the most commonly used metrics for evaluating a neural network.

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

Metrics

Chapter Goals: