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
-
demonstrate how to implement classification
list the various types of algorithms used in unsupervised learning
demonstrate how to implement K-Mean clustering
demonstrate how to implement hierarchical clustering
demonstrate how to facilitate text mining and work with recommender systems -
demonstrate the process involved in text mining and data assembly
specify the concepts of deep and reinforcement learning
work with Restricted Boltzmann machines
build models using Convolution Neural Network
utilize data frames and centroids
IN THIS COURSE
-
1.Working with Classification10m 7sUP NEXT
-
2.Unsupervised Learning5m 55s
-
3.K-Mean Clustering14m 50s
-
4.Hierarchical Clustering12m 9s
-
5.Text Mining and Recommender Systems11m 1s
-
6.Text Mining and Data Assembly9m 7s
-
7.Deep and Reinforcement Learning Concepts6m 27s
-
8.Restricted Boltzmann5m 36s
-
9.Working with CNN9m 13s
-
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
Digital badges are yours to keep, forever.YOU MIGHT ALSO LIKE
AUDIOBOOK
Deep Learning with Python, 2nd Edition
