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.

ml.tar.gz

mnist-fashion

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 find the nearest neighbors for a data observation</li>
</ul>

<h2>A. Finding the nearest neighbors</h2>
<p>In Chapter 1, we mentioned that clustering is a method for grouping together similar data observations. Another method for finding similar data observations is the <i>nearest neighbors</i> approach. With this approach, we find the <i>k</i> most similar data observations (i.e. neighbors) for a given data observation (where <i>k</i> represents the number of neighbors).</p>
<p>In scikit-learn, we implement the nearest neighbors approach with the <code><a href="https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors" target="_blank">NearestNeighbors</a></code> object (part of the <code>neighbors</code> module).</p>
<p>The code below finds the 5 nearest neighbors for a new data observation (<code>new_obs</code>) based on its fitted dataset (<code>data</code>).</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to find the nearest neighbors for a data observation</li>
</ul>

Understand the purpose of finding nearest neighbors for data points.

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

Nearest Neighbors

Chapter Goals:

A. Finding the nearest neighbors