Supervised, Unsupervised & Deep Learning
Python
| 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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10m 7sIn this video, you will learn how to implement a classification. FREE ACCESS
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5m 55sUpon completion of this video, you will be able to list the various types of algorithms used in unsupervised learning. FREE ACCESS
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14m 50sIn this video, you will learn how to implement K-Means clustering. FREE ACCESS
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12m 9sIn this video, you will learn how to implement hierarchical clustering using the SciPy library. FREE ACCESS
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11m 1sIn this video, you will learn how to text mine and work with recommender systems. FREE ACCESS
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9m 7sIn this video, you will learn how to apply the process involved in text mining and data assembly. FREE ACCESS
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6m 27sUpon completion of this video, you will be able to specify the concepts of deep and reinforcement learning. FREE ACCESS
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5m 36sDuring this video, you will learn how to work with Restricted Boltzmann Machines. FREE ACCESS
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9m 13sIn this video, you will build models using a Convolution Neural Network. FREE ACCESS
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6m 11sIn this video, find out how to use data frames and centroids. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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