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>Learn how to use agglomerative clustering for feature dimensionality reduction</li>
</ul>

<h2>A. Agglomerative feature clustering</h2>
<p>In the <b>Data Preprocessing</b> section, we used PCA to perform feature dimensionality reduction on datasets. We can also perform feature dimensionality reduction using agglomerative clustering. By merging common features into clusters, we reduce the number of total features while still maintaining most of the original information from the dataset.</p>
<p>In scikit-learn, we perform agglomerative clustering on features using the <code><a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html#sklearn.cluster.FeatureAgglomeration" target="_blank">FeatureAgglomeration</a></code> object (part of the <code>cluster</code> module). When initializing the object, <code>n_clusters</code> keyword argument (which represents the number of final clusters) is used to specify the new feature dimension of the data.</p>
<p>The code below demonstrates how to use the <code>FeatureAgglomeration</code> object to reduce feature dimensionality from 4 to 2. We use the object's <code>fit_transform</code> function to fit the clustering model on the data, then subsequently apply the feature reduction on the data.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to use agglomerative clustering for feature dimensionality reduction</li>
</ul>

Use agglomerative clustering for feature dimensionality reduction.

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 Clustering

Chapter Goals: