Supervised, Unsupervised & Deep Learning
Python 3.6.5
| Intermediate
- 10 Videos | 1h 30m 37s
- Includes Assessment
- Earns a Badge
Discover how to implement various supervised and unsupervised algorithms of machine learning using Python, with the primary focus of clustering and classification.
WHAT YOU WILL LEARN
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demonstrate how to implement classificationlist the various types of algorithms used in unsupervised learningdemonstrate how to implement K-Mean clusteringdemonstrate how to implement hierarchical clusteringdemonstrate how to facilitate text mining and work with recommender systems
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demonstrate the process involved in text mining and data assemblyspecify the concepts of deep and reinforcement learningwork with Restricted Boltzmann machinesbuild models using Convolution Neural Networkutilize data frames and centroids
IN THIS COURSE
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1.Working with Classification10m 7sUP NEXT
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2.Unsupervised Learning5m 55s
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3.K-Mean Clustering14m 50s
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4.Hierarchical Clustering12m 9s
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5.Text Mining and Recommender Systems11m 1s
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6.Text Mining and Data Assembly9m 7s
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7.Deep and Reinforcement Learning Concepts6m 27s
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8.Restricted Boltzmann5m 36s
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9.Working with CNN9m 13s
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10.Exercise: Working with Data Frames and Centroids6m 11s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
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COURSE
Final Exam: Pythonista