Master advanced clustering, topic modeling, manifold learning, and autoencoders using Unsupervised Learning with Python!
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Explore various Python libraries, including numpy, pandas, scikit-learn, matplotlib, seaborn and plotly
Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets.
Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data.
Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning.
Compare and evaluate the results of different data analyses to determine the quality of clusters, time, and memory usage.
This comprehensive Unsupervised Learning with Python: Step-by-Step Tutorial! curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
What you need before starting this Unsupervised Learning with Python: Step-by-Step Tutorial! course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Packt Publishing
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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