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Advance Machine Learning

Instructor Led Training Program (Online)

  • Duration 180 day
  • 400 Hour(s)
  • Level: Advanced
  • Paid Internship
  • Free Job Consultancy
  • Assignments and Exams
  • 9 Course(s)
  • Live Industry Project

Fee : Rs 30000
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Overview

Machine learning is a method of data analysis that automates analytical model building through experience and by the use of data.
It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
About the Program : The online course on Machine Learning can make you Ariticial Intelligence expert, since AI is the most upcoming Trend in market and a topic on Advance Computer Science you will learn Computer Science and also can boost your career. It offers you complete guidance and tutorial with experience teachers, downloadable resources, hands-on learning experience and job/ internship consultancy.

The total course tenure 6 months divided into 3 months training and 3 months internship.



Fee : Rs 30000
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Course Contents

Project related practical..

We use unsupervised learning to build models that help us understand our data better. Concepts covered in this subject area are as follows:

01.Introduction
02.K-Means Algorithm
03.Optimization Objective
04.Implementation Note: Unrolling Parameters
05.Random Initialization
06.Choosing the Number of Clusters
06.Practice Exercise
06.Project

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data. So Module 1 includes the following :

01.Welcome to Machine Learning
02.Machine Learning Honor Code
03.Supervised Learning
04.Unsupervised Learning
05.Practice exercise

Linear regression predicts a real-valued output based on an input value :

01.Cost Function
02.Intuition I and Intuition II
03.Difference between population and sample
04.Gradient Descent
05.Gradient Descent For Linear Regression
06.Practice exercise

This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course. Gothrough the sub divisions :

01.Matrices and Vectors
02.Addition and Scalar Multiplication
03.Matrix Vector Multiplication
04.Matrix Multiplication Properties
05.Inverse and Transpose
06.Practice Exercise

What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features :

01.Multiple Features
02.Gradient Descent for Multiple Variables
03.Gradient Descent in Practice I and Practice II
04.Features and Polynomial Regression
05.Normal Equation
06.Normal Equation
07.Practice Exercise

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets. we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition. Concepts covered in this subject area are as follows:

01.Cost Function
02.Backpropagation Algorithm
03.Backpropagation Intuition
04.Implementation Note: Unrolling Parameters
05.Gradient Checking
06.Random Initialization
07.Practice Exercise

To optimize a machine learning algorithm, you'll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data. Concepts covered in this subject area are as follows:

01.Cost Function
02.Backpropagation Algorithm
03.Backpropagation Intuition
04.Implementation Note: Unrolling Parameters
05.Gradient Checking
06.Random Initialization
07.Practice Exercise

Project related test.

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    Application Process

    Candidates can apply to this Advance Machine Learning in 3 steps.

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    Admission

    After the payment is completed, the application will be finally approved and you can start learning.

    Live Industry Projects View All Projects

    Library Management System

    Library management system project website used to maintain all activities of library system such as maintain books stock, issue book records, return book records, student detail and also keep data of penalty for late return books. The project covered all activities which have done to run the library system. 

    E-Governance Project

    e-Governance refers to the use of Information and Communication
    Technologies to improve the Transparency, Accountability, Efficiency & Effectiveness of Government.

    • Doing things differently, and not doing different things…
    • Re-engineered process perspective
    • Citizen and service-centric approach, and not a department centric
    • ICT Enablement
    • The back-ends and in parallel creating suitable middleware
    • community access points.

    Virtual Laboratory

    Virtual Laboratory is a computer-based activity where students interact with an experimental apparatus, hands-on or other activity via a computer interface.
    IT is also can be referred as an on-screen simulator or calculator that helps test ideas and observe results.  Virtual Labs enable the students to learn at
    their own pace and enthuse them to conduct experiments.
    Virtual Labs also provide a complete learning management system where
    the students can avail various tools for learning, including additional
    web resources, video lectures, animated demonstration, and
    self-evaluation.

    Our future plan is to use technologies like VR, AR, Meta Phase like technologies to make the feel of the experiments much more better.

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