Description
Welcome ​to an introduction to using Artificial Intelligence and Machine Learning with a focus on Amazon Web services and the Google Cloud platform. This course​ is designed to be a gentle introduction, starting at the ground up and focusing on giving students the tools and materials they need to navigate the topic. It will also include the necessary skills around data engineering, cloud management and even some systems engineering. 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.

This course begins with a introduction to AI and ML, before moving onto explain the different levels of users in the field. Then we take a look at out-of-the-box solutions for AI and ML, before looking at a case study to give you the topics covered during this course in a real-world example.

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, and for those wanting to become data engineers. 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/69b6404b95bcd649dcf6fec3d7a2e75c/6a2hm.Introduction.to.Machine.Learning.Part.One.rar.html

nitroflare_com:
https://nitroflare.com/view/6CE4F2343A56D15/6a2hm.Introduction.to.Machine.Learning.Part.One.rar

uploadgig_com:
http://uploadgig.com/file/download/fed0f48C8eF5Bfb9/6a2hm.Introduction.to.Machine.Learning.Part.One.rar