Welcome to Huddlerise Academy
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$50.0
170 SEATS

Course Details

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.

2 months Intensive Training By Real Time Experts

Requirements

  • Processor – Intel Xeon E2630 v4 – 10 core processor, 2.2 GHz with Turboboost upto 3.1 GHz.
  • Motherboard – ASRock EPC612D8A.
  • RAM – 128 GB DDR4 2133 MHz.
  • 2 TB Hard Disk (7200 RPM) + 512 GB SSD.
  • GPU – NVidia TitanX Pascal (12 GB VRAM)
  • Intel Heatsink to keep temperature under control.
  • The system should require C, Java, Python and Matlab knowledge to maintenance.
  • If any problem acquire in server side and deep learning methods, it requires code knowledge and deep learning background to solve.
  • Client side problems should be fixed with an update and it also require code knowledge and network knowledge.

Lessons 1: Introduction

  • Notation of Dataset
  • Training Set and Test Set
  • No Free Lunch Rule
  • Relationships with Other Disciplines
  • Designing versus Learning
  • The Categorization of Machine learning
  • The Structure of Learning
  • What are We Seeking?
  • The Optimization Criterion of Supervised Learning
  • The Strategies of Supervised Learning
  • The VC bound and Generalization Error
  • Three Learning Effects
  • Feature Transform
  • Model Selection
  • Three Learning Principles
  • Supervised Learning Overview
  • Linear Model (Numerical Functions)
  • Perceptron Learning Algorithm (PLA) – Classification
  • From Linear to Nonlinear
  • Adaptive Perceptron Learning Algorithm (PLA) – Classification
  • Linear Regression – Regression
  • Rigid Regression – Regression
  • Support Vector Machine (SVM) and Regression(SVR)
  • John Doe

    Professor

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    Naila Naime

    Bachelor

    There are many variations of passages of Lorem Ipsum available, but the majority have suffered altera tion in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum

    Reviews

    Moulali

    Jun 15,2018

    Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

    Rishi

    Aug 21, 2019

    It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

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