Machine Learning 101 : Introduction to Machine Learning

What you will learn.

The problem of learning. Learning from data. Is itsible to learn? The model is linear. Error and noise. Testing versus training. There is a theory of generalization. The dimensions of the VC. There is a Bias-Variance Tradeoff. Neural networks. Overfitting. Regularization is done. There is validation. Support the machines. The methods are called kernels. The basis functions are radial. There are three learning principles. Thelogue. What is learning? Can a machine learn? Understand basic theoretical principles and applications of machine learning. There are connections between theory and practice in machine learning. Understand the mathematical aspects of Machine Learning and their applications.

There are requirements.

Anyone who is interested in machine learning can take this course.

There is a description.

There is an introduction to machine learning.

Machine Learning 101 is about machine learning.

The course covers theory and applications of machine learning.

The basics of machine learning are covered in this course. Many financial, medical, commercial, and scientific applications use machine learning. Computational systems can improve their performance with experience accumulated from observed data. One of the hottest fields of study today is ML, which is taken up by undergraduate and graduate students from 15 different majors. Theory and practice are covered in this course, as well as the mathematical aspects. In a story-like fashion, the lectures below follow each other.

Get it immediately. Machine Learning 101 is about machine learning.

What is learning?

Can a machine learn?

How do I do it?

How do you do it well?

Lessons at home.

There is an outline of this course.

The Learning Problem is the first lecture.

The second lecture is about learning feasible.

The Linear Model I is the third lecture.

Error and noise are discussed in the fourth lecture.

Training versus testing is the topic of the fifth lecture.

Theory of Generalization is the sixth lecture.

The VC Dimension is the topic of the 7th lecture.

There is a Bias-Variance Tradeoff.

The Linear Model II is the ninth lecture.

Neural networks are the topic of the 10th lecture.

Overfitting is the topic of the 11th lecture.

The 12th lecture is called Regularization.

The 13th lecture is called validation.

Support Vector Machines is the 14th lecture.

The 15th lecture is called Kernel Methods.

The 16th lecture is called Radial Basis Functions.

There are three learning principles.

The Epilogue is the 18th lecture.

Some of the videos on the course have a Creative Commen Licence. It’s not like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it Who this course is for?

You can attend this lesson if you have no coding experience. It’s not like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it’s like it Anyone who is interested in data science. Anyone interested in learning from data. Anyone interested in how deep learning works. The data science and machine learning career path will be learned from this course.