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
You will learn about :-
-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 8: Bias-Variance Tradeoff
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 16: Radial Basis Functions
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