پیشنهاد فرادرس

آموزش تسلط بر یادگیری بدون نظارت با پایتون

دسته بندی ها: آموزش پایتون (Python) ، آموزش های Packtpub

در این دوره ویدئویی شما مفاهیم، ​​مزایا و معایب مختلف الگوریتم های خوشه بندی محبوب را درک خواهید کرد و با اعمال آنها به مجموعه های داده های مختلف، اعمال الگوریتم Latent Dirichlet Allocation به مدل سازی موضوعات، T-SNE و UMAP، بررسی K-Means، خوشه بندی مبتنی بر تراکم و مدل های مخلوط Gaussian، استراتژی های bottom-up و top-down، پیش پردازش متن، مفاهیم مورد نیاز برای اطمینان از تسلط بر الگوریتم های بدون نظارت و غیره آشنا می شوید.

سرفصل:

  • معرفی دوره
  • جایگزینی برای خوشه بندی K-Means
  • خوشه بندی آگلومره: یافتن سلسله مراتب طبیعی
  • خوشه بندی مبتنی بر تراکم: DBSCAN و HDBSCAN
  • مدل های ترکیبی Gaussian
  • مدل سازی موضوع: توصیه های محتوای معناشناختی
  • مدل سازی موضوع: بررسی
  • مدل سازی موضوع: آماده سازی داده های شما
  • مدل سازی موضوع: اجرای مدل ها
  • مدلسازی opic: ارزیابی و تجسم نتایج
  • یادگیری عمیق
  • یادگیری Manifold
  • یادگیری عمیق و مصورسازی: Autoencoders و T-SNE
  • و غیره
به این نوشته امتیاز دهید 1 2 3 4 5 بدون امتیاز
Mastering Unsupervised Learning with Python [Video] Publisher:Packtpub Author:Stefan Jansen Duration:3 hours 52 minutes

Master advanced clustering, topic modeling, manifold learning, and autoencoders using Python
In this video course you will understand the assumptions, advantages, and disadvantages of various popular clustering algorithms, and then learn how to apply them to different data sets for analysis. You will apply the Latent Dirichlet Allocation algorithm to model topics, which you can use as an input for a recommendation engine just like the New York Times did. You will be using cutting-edge, nonlinear dimensionality techniques (also called manifold learning)—such as T-SNE and UMAP—and autoencoders (unsupervised deep learning) to assess and visualize the information content in a higher dimension. You will be looking at K-Means, density-based clustering, and Gaussian mixture models. You will see hierarchical clustering through bottom-up and top-down strategies. You will go from preprocessing text to recommending interesting articles. Through this course, you will learn and apply concepts needed to ensure your mastery of unsupervised algorithms in Python.
By the end of this course, you will have mastered the application of Unsupervised Learning techniques and will be able to utilize them in your Data Science workflow—for instance, to extract more informative features for Supervised Learning problems. You will be able not only to interpret results but also to enhance them.
After having taken this course, you will have mastered the application of Unsupervised Learning with Python. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Mastering-Unsupervised-learning-with-Python
Style and Approach
An exhaustive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks, so you can learn at your own pace and focus on your area of interest.
Released: Tuesday, August 14, 2018
Advanced Clustering Methods: Selecting the Best Algorithm
The Course Overview
Alternatives to K-Means Clustering – Part 1
Alternatives to K-Means Clustering – Part 2
Agglomerative Clustering: Finding Natural Hierarchies – Part 1
Agglomerative Clustering: Finding Natural Hierarchies –Part 2
Density-Based Clustering: DBSCAN and HDBSCAN – Part 1
Density-Based Clustering: DBSCAN and HDBSCAN – Part 2
Gaussian Mixture Models
Topic Modeling: Semantic Content Recommendations
Topic Modeling: Overview – Part 1
Topic Modeling: Overview – Part 2
Topic Modeling: Preparing Your Data – Part 1
Topic Modeling: Preparing Your Data – Part 2
Topic Modeling: Running the Models – Part 1
Topic Modeling: Running the Models – Part 2
Topic Modeling: Evaluating and Visualizing Results
Manifold and Deep Learning for High-Dimensional Data
Manifold Learning: Introduction – Part 1
Manifold Learning: Introduction – Part 2
Manifold Learning in Practice – Part 1
Manifold Learning in Practice – Part 2
Visualize High-Dimensional Data: t-SNE and UMAP – Part 1
Visualize High-Dimensional Data: t-SNE and UMAP – Part 2
Deep Learning and Visualization: Autoencoders and t-SNE – Part 1
Deep Learning and Visualization: Autoencoders and t-SNE – Part 2

پیشنهاد فرادرس

لینک های دانلود حجم فایل: 803.0MB Packtpub Mastering Unsupervised Learning with Python [Video]_git.ir.rar