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 K-means clustering and how it works</li>
  <li>Understand why mini-batch clustering is used for large datasets</li>
</ul>

<h2>A. K-means algorithm</h2>
<p>The idea behind clustering data is pretty simple: partition a dataset into groups of similar data observations. How we go about finding these clusters is a bit more complex, since there are a number of different methods for clustering datasets.</p>
<p>The most well-known clustering method is <a href="https://en.wikipedia.org/wiki/K-means_clustering" target="_blank">K-means clustering</a>. The K-means clustering algorithm will separate the data into <i>K</i> clusters (the number of clusters is chosen by the user) using cluster means, also known as <i>centroids</i>.</p>
<p>These centroids represent the "centers" of each cluster. Specifically, a cluster's centroid is equal to the average of all the data observations within the cluster.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about K-means clustering and how it works</li>
  <li>Understand why mini-batch clustering is used for large datasets</li>
</ul>

cluster = np.array([
  [ 1.2, 0.6],
  [ 2.4, 0.8],
  [-1.6, 1.4],
  [ 0. , 1.2]])
print('Cluster:\n{}\n'.format(repr(cluster)))

centroid = cluster.mean(axis=0)
print('Centroid:\n{}\n'.format(repr(centroid)))

<p>The K-means clustering algorithm is an iterative process. Each iteration, the algorithm will assign each data observation to the cluster with the closest centroid to the observation (using the regular <a href="http://mathworld.wolfram.com/Distance.html" target="_blank">distance</a> metric).</p>
<p>Then it updates each centroid to be equal to the new average of the data observations in the cluster. Note that at the beginning of the algorithm, the cluster centroids are either randomly initialized or (better) initialized using the <a href="https://en.wikipedia.org/wiki/K-means%2B%2B" target="_blank">K-means++</a> algorithm. The clustering process stops when there are no more changes in cluster assignment for any data observation.</p>

Learn about the K-Means clustering algorithm and how it works.

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

K-Means Clustering

Chapter Goals:

A. K-means algorithm