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.:PROUDLY PRESENTS:.
Cloud Academy Introduction to Machine Learning - Part Two
Release Date.: 08-07-2020
Type.: Bookware
Disks.: 28x50mb
Link.: https://cloudacademy.com
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▐█████▓▓▌ Release Notes ▐▓▓█████▌
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Welcome ?to Part Two of an introduction to using Artificial
Intelligence and Machine Learning. As we mentioned in part
one, this course starts at the ground up and focuses on
giving students the tools and materials they need to
navigate the topic. There are several labs directly tied to
this learning path, which will provide hands on experience
to supplement the academic knowledge provided in the lectures.
In part one we looked at how you can use out-of-the-box
machine slearning models to meet your needs. In this
course, we are going to build on that and look at how you
can add your own functionality to these pre-canned models.
We look at ML training concepts, release processes, and how
ML services are used in a commercial setting. Finally, we
take a look at a case study so that you get a feel for how
these concepts play out in the real world.
For any feedback relating to this course, please contact us
at support@cloudacademy.com.
Learning Objectives
By the end of this course, you'll hopefully understand how
to take more advanced courses and even a springboard into
handling complex tasks in your day to day job, whether it
be a professional, student, or hobbyist environment.
Intended Audience
This course? is a multi-part series ideal for those who are
interested in understanding machine learning from a 101
perspective; starting from a very basic level and ramping
up over time. If you already understand concepts such as
how to train and inference a model, you may wish to skip
ahead to part two or a more advanced learning path.
Prerequisites
It helps if you have a light data engineering or developer
background as several parts of this class, particularly the
labs, involve hands-on work and manipulating basic data
structures and scripts. The labs all have highly detailed
notes to help novice users understand them but you will be
able to more easily expand at your own pace with a good
baseline understanding. As we explain? the core concepts,
there are some prerequisites for this course.
It is recommended that you have a basic familiarity with
one of the cloud providers, especially AWS or GCP. Azure,
Oracle and other providers also have machine learning
suites but these two are the focus for this class.
If you have an interested completing the labs for hands on
work, Python is a helpful language to understand. Now, if
you're looking into a career in machine learning, you can
definitely do it with languages such as Java, C#, even
lower level languages such a C++ or functional languages
such as R or Matlab. However, in my experience, Python is
the most widely adopted language specifically, if you're
looking to go heavy duty into training, learning, and
developing models,
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▄▓▓█▓▓██▄ Kodemusen, KoseBamsen ▄██▓▓█▓▓▄
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▄▓▓██ ▓▓██▄ STM is back. ▄██▓▓ ██▓▓▄
▄▓▓██ ▓▓██▄ For all the ppl we worked with ▄██▓▓ ██▓▓▄
▄▓▓██▀ ▓▓██▄ in the past. We salute you. ▄██▓▓ ▀██▓▓▄
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▀▓▓█ ▐████▓▓▓▌ NFO by NiMiTech ▐▓▓▓████▌ █▓▓▀
▀▓▓█▄ ▐█████▓▓▌ Updated: 09/09/2002 ▐▓▓█████▌ ▄█▓▓▀
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