December 27 2019

Introduction to Machine Learning -Top University Professor

Introduction to Machine Learning -Top University Professor
Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Duration: 17:54:52
Genre: eLearning | Language: English + Subtitles | Size: 15.6 GB

and Much MUch More!

with Python Exercises - 18Hours of HD Video 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
Beginners are Welcome!
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 beginning 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.
So for example if you talk about answering questions in an exam your performance criterion could very well be the number of marks that you get or if you talk about diagnosing illness then your performance measure would be the number of patients that you say are the number of patients who did not have adverse reaction to the drugs you gave them there could be variety of ways of defining performance measures depending on what you are looking for right and the third important component here is experience right.
So with experience the performance has to improve right and so what we mean by experience here in the case of writing exams it could be writing more exams right so the more the number of exams you write the better you write it better you get it test taking or it could be a patient's in the case of diagnosing illnesses like the more patients that you look at the better you become at diagnosing illness right.
So these are the three components so you need a class of tasks you need a performance measure and you need some well-defined experience so this kind of learning right where you are learning to improve your performance based on experience is known as a this kind of learning where you are trying to where you learn to improve your performance with experience is known as inductive learning. And then the basis of inductive learning goes back several centuries people have been debating about inductive learning for hundreds of years now and are only more recently we have started to have more quantified mechanisms of learning right. So but one thing I always point out to people is that if you take this definition with a pinch of salt, so for example you could think about the task as fitting your foot comfortably right.



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