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>Apply K-Fold cross-validation to a decision tree</li>
</ul>

<h2>A. Decision tree depth</h2>
<p>We've previously discussed cross-validation for tuning hyperparameters such as the &alpha; value for regularized regression. For decision trees, we can tune the tree's maximum depth hyperparameter (<code>max_depth</code>) by using K-Fold cross-validation.</p>
<p>K-Fold cross-validation gives an accurate measurement of how good the decision tree is for the dataset. We can use K-Fold cross-validation with different values of the <code>max_depth</code> hyperparameter and see which one gives the best cross-validation scores.</p>
<p>The code below demonstrates how to apply K-Fold CV to tune a decision tree's maximum depth. It uses the <code>cv_decision_tree</code> function that you will implement later in this chapter.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Apply K-Fold cross-validation to a decision tree</li>
</ul>

Apply K-Fold cross-validation to decision trees.

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

Applying CV to Decision Trees

Chapter Goals:

A. Decision tree depth