Delve into practical machine learning with NumPy, pandas, scikit-learn, and more. Gain insights into data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

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If you're a software engineer looking to add machine learning to your skillset, this is the place to start. 

This course will teach you to write useful code and create impactful machine learning applications immediately. From the start, you'll be given all the tools that you need to create industry-level machine learning projects. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All of these topics are taught using industry-standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Basic knowledge of Python is a prerequisite to this course.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Machine Learning with NumPy, pandas, scikit-learn, and More

<h2 style="font-size:1.1em">Chapter Goals:</h2>
<ul>
  <li>Understand how to measure each dataset feature's importance in making model predictions</li>
  <li>Use the matplotlib pyplot API to save a feature importance plot to a file</li>
</ul>

<h2>A. Determining important features</h2>
<p>Not every feature in a dataset is used equally for helping a boosted decision tree make predictions. Certain features are more important than others, and it is useful to figure out which features are the most important.</p>
<p>After training an XGBoost model, we can view the relative (proportional) importance of each dataset feature using the <code>feature_importances_</code> property of the model.</p>
<p>The code below prints out the relative feature importances of a model trained on a dataset of 4 features.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Understand how to measure each dataset feature's importance in making model predictions</li>
  <li>Use the matplotlib pyplot API to save a feature importance plot to a file</li>
</ul>

Learn about feature importance in making model predictions.

What you'll learn from this course

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

Feature Importance

Chapter Goals:

A. Determining important features