Python and Machine Learning Primer

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Python and Machine Learning Primer

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 42M | 348 MB

Genre: eLearning | Language: English

 

Master the basics of Python and machine learning concepts. Write Python scripts against data sets to implement machine learning algorithms. This video series contains four clips:

 

Networking. This second clip in the Python and Machine Learning Primer explains in detail the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP).

 

Master the basics of Python and machine learning concepts. Write Python scripts against data sets to implement machine learning algorithms. This video series contains four clips:

Python Essentials. This first clip in the Python and Machine Learning Primer covers the basics of Python, including command syntax, variable types, basic operations, decision making, loops, strings, lists, dictionaries, functions, and classes.

Networking. This second clip in the Python and Machine Learning Primer explains in detail the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). Important Python networking concepts are covered including sockets and ports.

Threads. This third clip in the Python and Machine Learning Primer focuses on how to create and manipulate threads and perform multitasking. The key thread commands discussed are run( ), start( ), isAlive( ), getName( ), and setName( ). A number of thread models are explained including peer-to-peer models, delegation or manager-worker model, producer-consumer model, and the pipeline threading model.

Machine Learning. This fourth clip in the Python and Machine Learning Primer explores machine learning in depth. Learn how traditional programming compares with machine learning. Know the different machine learning representations including decision trees, logic programs, graphical models (Bayes/Markov nets), neural networks, support vector machines, and model ensembles. Differentiate the types of machine learning including supervised (inductive) learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Apply machine learning using Python against several data sets.

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