Machine Learning 101 : Introduction to Machine Learning

In this article, you will discuss the interconnections between theory and practice. Machine Learning Learn the mathematics and heuristic sides of Machine Learning File size: 17.07GB

Machine Learning 101 : Introduction to Machine Learning

What you will learn

    The Learning Problem
Learning Data
It is Learning Feasible?
The Linear Model
Errors and Noise
Training versus Testing
Theory of Generalization
The VC Dimension
Bias and Variance Tradeoff
Neural Networks
Overfitting
Regularization
Validation
Support Vector Machines
Kernel Methods
Radial Basis Functions
Three Learning Principles
Epilogue
What is learning?
Can a machine be taught?
Identify the fundamental theoretical principles, algorithms, or applications Machine Learning
In this article, you will discuss the interconnections between theory and practice. Machine Learning
Learn how to master the mathematical and heuristic aspects. Machine Learning Their applications to Real world situations

Requirements

Anyone who is interested Machine Learning This course is available to you

Description

Introduction to Machine Learning

Machine Learning 101 : Introduction to Machine Learning

Introductory Machine Learning Course covering theory, algorithms, and applications.

This is an introductory course on machine learning (ML), which covers the basics, algorithms, and application. ML is a crucial technology in Big Data and many other financial, medical, and scientific applications. It allows for computational systems to With the experience gained from the observation data, they can adapt to improve their performance. ML is a booming field that has attracted graduate and undergraduate students from 15 majors. This course combines theory and practice and includes both the mathematical and heuristic elements. These lectures are arranged in a narrative-like manner.

Download it immediately Machine Learning 101 : Introduction to Machine Learning

    What is learning?

    Can a machine learn?

How to do it?

How to Do it right?

Take-home lessons.

The outline of the course;

Lecture 1: Learning Problem

Lecture 2: Is Learning Feasible?

Lecture 3: The Linear Model II

Lecture 4: Errors, Noise

Lecture 5: Training versus Testing

Lecture 6: Theory of Generalization

Lecture 7: The VC Dimension

Lecture 8: Bias-Variance tradeoff

Lecture 9: Linear Model II

Lecture 10: Neural Networks

Lecture 11: Overfitting

Lecture 12: Regularization

Lecture 13: Validation

Lecture 14: Support Vector Machines

Lecture 15: Kernel Methods

Lecture 16: Radial Basis Functions

Lecture 17: Three Learning Principles

Lecture 18: Epilogue

Youtube has several videos from this course that have Creative Commen Licence (CC).
This course is designed for the following:

You can attend the lesson even if you don’t have any programming or scripting knowledge.
Anyone interested in Data Science
Anyone who is interested Learning Data
If you are interested in how deep learning works,
Programmers and software developers who are interested in programming to This course is a great way to learn how to get into the data science/machine learning career.