Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks

What you’ll study

  • Understanding regression difficulties
  • Learning organization difficulties
  • Using neural systems
  • The most inclusive up to date machine training methods used by firms so as Google or Facebook
  • Face detection with OpenCV
  • TensorFlow


  • Fundamental python


This course is about the primary ideas of machine training, concentrating on regression, SVM, result trees and neural systems. These problems are becoming very hot now because these training algorithms can be used in various fields from software engineering to support business. Training algorithms can identify models which can improve detect disease for example or we may create algorithms that can have a very great opinion about capital prices change in the business.

In every part we will discuss about the technical background for all of these algorithms then we are continuing to work these problems collectively. We will do Python with SklearnKeras and TensorFlow.

  • Machine Learning Algorithms: regression and division difficulties with Linear Regression, Logistic Regression, Simple Bayes Classifier, kNN algorithm, Support Vector Tools (SVMs) and Decision Trees
  • Machine Learning progresses in economics: how to use training algorithms to prophesy produce prices
  • Computer Vision and Face Detection with OpenCV
  • Neural Networks: what are feed-forward neural networks and why are they helpful
  • Deep LearningRepetitive Neural Networks and Convolutional Neural Networks and their purposes such as opinion analysis or capital prices estimate
  • Support Learning: Markov Settlement methods (MDPs) and Q-learning

Gratefulness for following the course, let’s get excited!

Who this course is for:

  • This course is intended for newbies who are not easy with machine learning or seniors looking for a fast refresher

Designed by Holczer Balazs

Latest updated 4/2019


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