Explore the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

Become a Machine Learning Engineer

Start your journey toward becoming an ML engineer by learning practical ML skills such as data manipulation, data analysis, data preprocessing, data modeling, clustering, gradient boosting and deep learning. Basic knowledge about Python is a prerequisite. All topics are taught with industry-standard python frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Machine Learning for Software Engineers

<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.

Before We Begin

Are You Ready to Become a Machine Learning Engineer?

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

Conclusion

Introduction

A. Unsupervised learning