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

<p>In this chapter, continuing with scikit-learn, you will use unsupervised learning methods—methods of extracting insights from unlabeled datasets. Specifically, you'll learn about different clustering algorithms and how they are able to group together similar data observations.</p>

<h2>A. Unsupervised learning</h2>
<p>So far, we've used only supervised learning methods because we've exclusively been dealing with labeled datasets. However, in the real world, many datasets are completely unlabeled because labeling datasets involves additional work and foresight. Rather than ignoring all these unlabeled datasets, we can still extract meaningful insights using unsupervised learning.</p>
<p>Since we have only data observations to work with and no labels, unsupervised learning methods are centered around finding similarities/differences between data observations and making inferences based on those findings. The most commonly used form of unsupervised learning is <a href="https://en.wikipedia.org/wiki/Cluster_analysis" target="_blank">clustering</a>. As the name suggests, clustering algorithms gather data into distinct groups (clusters), where each cluster consists of similar data observations.</p>
<p>Clustering is used in many different applications, from anomaly detection (detecting real vs. fraudulent data) to market research (grouping customers together based on their purchase history). In the upcoming chapters, you'll learn about a variety of clustering algorithms commonly used in data science, as well as other tools for finding similarities between data observations.</p>

An overview of unsupervised learning and clustering.

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

Introduction

A. Unsupervised learning