Course Description

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

What am I going to get from this course?

Over 5 lectures and 47 mins of content!
In this course you will learn different learning concepts such as K- nearest neighbor learning , non symbolic machine learning etc.,neural network
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition

What is the target audience?

Students
Professionals interested in biometrics, expert system and robotics industry.

Curriculum

Section 1: Overview of Machine Learning

1                  Introduction to Machine Learning

2                  Perceptron Learning

3                  Perceptron learning linear separable case

Section 2: Learning about Backpropagation

4                  Backpropagation with multilayer neuron

5                  W for hidden node and backpropagation algo

Section 3: backpropagation and algorithm

6                  backpropagation algorithm explained

7                  backpropagation calculation_part01

8                  backpropagation calculation_part02

9                  updation of weight and cluster

10               k-means cluster,NNalgo and appliaction of machine learning

Here is a sample for the course completion certificate which you will recieve after complete the course. This certificate is widely accepted across industries and will boost your chances to grab the job opportunities.

Mail us at: [email protected] with below details to receive your certificate:

• Name
• Course Name
• Date of Completion
• Address