Description
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.

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,

Screenshots

Citación :

rapidgator_net:
https://rapidgator.net/file/5e5f77c74260d5162c5c97322dc03b0b/hoaxj.Introduction.to.Machine.Learning..Part.Two.part1.rar.html
https://rapidgator.net/file/4fc13af10b5fe77cba7ca960e73e71b4/hoaxj.Introduction.to.Machine.Learning..Part.Two.part2.rar.html

nitroflare_com:
https://nitroflare.com/view/EDD9DDE745CEEDB/hoaxj.Introduction.to.Machine.Learning..Part.Two.part1.rar
https://nitroflare.com/view/A6451CCBC95B8A6/hoaxj.Introduction.to.Machine.Learning..Part.Two.part2.rar

uploadgig_com:
http://uploadgig.com/file/download/125ebd2CB6c60E0f/hoaxj.Introduction.to.Machine.Learning..Part.Two.part1.rar
http://uploadgig.com/file/download/A75803715aa5907a/hoaxj.Introduction.to.Machine.Learning..Part.Two.part2.rar