# Machine Learning 101 : Introduction to Machine Learning

Short Description: – In this 25+ hour course you will learn all about machine learning that will cover theory, algorithms and application

The requirement’s before enrolling this course:-

-Basic knowledge of machine learning
-Python
-Interest in Machine learning

-To learn to solve the learning problem
-Learning form the data
-Epilogue
-How to use VC Dimension
-Theroy of Generalization
-How to solve error and noise
-How to identify the basic theoretical principles along with algorithm and application of the machine learning
-Elaborating the connection between theory and practicing in machine learning
-How to master mathematics and heuristics aspects to solve some real wold situation

Description:-

In this “Machine Learning 101 : Introduction to Machine Learning” introductory course you will learn about the basic of theory, algorithm, and application. Machine Learning is a important and key technology that is used in Big Data and also in medical , scientific, and commercial application. Machine Learning helps the computational system and to improve their performance by the help of experience that is obtained from the observed data.

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 in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:

Machine Learning is the biggest and hottest field currently in the market and it tends to improve in near future and in this course many lectures are provided:

Lecture 1: The Learning Problem

Lecture 2: Is Learning Feasible?

Lecture 3: The Linear Model I

Lecture 4: Error and Noise

Lecture 5: Training versus Testing

Lecture 6: Theory of Generalization

Lecture 7: The VC Dimension

Lecture 9: The 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 17: Three Learning Principles

Lecture 18: Epilogue

So don’t wait and enroll in this course as soon as possible

Who should enroll this course:-

-Anyone want to learn Machine Learning
-Python expert
-Anyone who want to make career in Machine Learning

This course provides:-

-12 Articles and resources
-25+ hour of course
-Access on platform like Mobile and T.V.
-Certificate after completion of this course

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