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 about the mean shift clustering algorithm</li>
</ul>

<h2>A. Choosing the number of clusters</h2>
<p>Each of the clustering algorithms we've used so far require us to pass in the number of clusters. This is fine if we already know the number of clusters we want, or have a good guess for the number of clusters. However, if we don't have a good idea of what the actual number of clusters for the dataset should be, there exist algorithms that can automatically choose the number of clusters for us.</p>
<p>One such algorithm is the <a href="https://en.wikipedia.org/wiki/Mean_shift" target="_blank">mean shift</a> clustering algorithm. Like the K-means clustering algorithm, the mean shift algorithm is based on finding cluster centroids. Since we don't provide the number of clusters, the algorithm will look for "blobs" in the data that can be potential candidates for clusters.</p>
<p>Using these "blobs", the algorithm finds a number of candidate centroids. It then removes the candidates that are basically duplicates of others to form the final set of centroids. The final set of centroids determines the number of clusters as well as the dataset cluster assignments (data observations are assigned to the nearest centroid).</p>
<p>In scikit-learn, the mean shift algorithm is implemented with the <code><a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html" target="_blank">MeanShift</a></code> object (part of the <code>cluster</code> module). Since the algorithm doesn't require us to pass in the number of clusters, we can initialize the <code>MeanShift</code> with no arguments.</p>
<p>The code below demonstrates how to use the <code>MeanShift</code> object.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about the mean shift clustering algorithm</li>
</ul>

Use mean shift clustering to determine the optimal number of clusters.

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

Mean Shift Clustering

Chapter Goals: