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Machine Learning & Python & Data Science -140 Hours HD Video

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  • Datum pokretanja
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UPLOADER
Učlanjen(a)
01.05.2019
Poruka
39.564

Video: .MP4, 1280x720 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 139:57:45
Genre: eLearning | Language: English + Subtitles | Size: 117 Gb (Total)

and Much MUch More!​

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What you'll learn

Hypothesis Space and Inductive Bias

Evaluation and Cross-Validation

Linear Regression

Learning Decision Tree

Python Exercise on Decision Tree and Linear Regression

!

Requirements

Bners are Welcome!

Description

Hello everyone and welcome to this course on an introduction to machine learning

in this course we will have a quick introduction to machine learning and this will not be very deep in a mathematical sense but it will have some amount of mathematical trigger and what we will be doing in this course is covering different paradigms of machine learning and with special emphasis on classification and regression tasks and also will introduce you to various other machine learning paradigms. In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning. )

A brief introduction with emphasis on brief right, so the rest of the course would be a more elongated introduction to machine learning right.

So what is machine learning so I will start off with a canonical definition put out by Tom Mitchell in 97 and so a machine or an agent I deliberately leave the bning undefined because you could also apply this to non machines like biological agents so an agent is said to learn from experience with respect to some class of tasks right and the performance measure P if the learners performance tasks in the class as measured by P improves with experience.

So what we get from this first thing is we have to define learning with respect to a specific class of tasks right it could be answering exams in a particular subject right or it could be diagnosing patients of a specific illness right.

So but we have to be very careful about defining the set of tasks on which we are going to define this learning right, and the second thing we need is of a performance measure P right so in the absence of a performance measure P you would start to make vague statement like oh I think something is happening right that seems to be a change and something learned is there is some learning going on and stuff like that.

So if you want to be clearer about measuring whether learning is happening or not you first need to define some kind of performance criteria right.



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