Price: $79.99 - $52.17
(as of Dec 13,2024 15:17:40 UTC – Details)

From the brand

Explore our collection

Sharing the knowledge of experts

O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Publisher ‏ : ‎ O’Reilly Media; 1st edition (April 16, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 359 pages
ISBN-10 ‏ : ‎ 1492035645
ISBN-13 ‏ : ‎ 978-1492035640
Item Weight ‏ : ‎ 1.2 pounds
Dimensions ‏ : ‎ 7 x 0.7 x 9.1 inches

In this post, we will explore hands-on unsupervised learning using Python and learn how to build applied machine learning solutions from unlabeled data. Unsupervised learning is a type of machine learning where the model is trained on data without any explicit labels or target variable. This allows the model to find patterns and structure in the data on its own.

We will cover the following topics in this post:

1. Introduction to unsupervised learning: We will discuss what unsupervised learning is and how it differs from supervised learning. We will also explore common techniques used in unsupervised learning, such as clustering and dimensionality reduction.

2. Preprocessing unlabeled data: We will walk through the process of preprocessing unlabeled data, including handling missing values, scaling features, and encoding categorical variables.

3. Clustering: We will dive into clustering algorithms, such as K-means and hierarchical clustering, and learn how to implement them in Python using popular libraries like scikit-learn.

4. Dimensionality reduction: We will explore dimensionality reduction techniques, such as Principal Component Analysis (PCA) and t-SNE, and see how they can be used to visualize high-dimensional data and uncover hidden patterns.

5. Building applied machine learning solutions: Finally, we will put everything together and build applied machine learning solutions using unsupervised learning techniques. We will walk through a real-world example and show how unsupervised learning can be used to solve practical problems.

By the end of this post, you will have a solid understanding of unsupervised learning and how to apply it to build machine learning solutions from unlabeled data. So stay tuned for our hands-on tutorial on unsupervised learning using Python!
#HandsOn #Unsupervised #Learning #Python #Build #Applied #Machine #Learning #Solutions #Unlabeled #Data