Machine Learning Certification Training

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Machine Learning mainly focuses on the enhancement and development of the computer programs, which has the property to get changed when it comes in the interaction to the new data. However, this is a kind of artificial intelligence, the Introduction to Machine Learning course enlightens the candidates with the algorithms that proves to be helpful for the IP professionals in analyzing the data set with ease. In the training modules algorithms such as: regression, clustering, classification,  and recommendation have been introduced, all these helps the candidates in supervising the advanced data programing techniques.

After completing the introduction to machine learning certification the candidates would be able to:

  • Determine the various applications of machine learning algorithms
  • Develop an understanding classification data and models
  • Learn the how to implement the unsupervised learning algorithms, which  includes deep learning, clustering, and recommendation systems
  • How to perform the supervised learning techniques, such as: linear and logistic regression?
Target audience
  • Analytics professionals
  • Data Science professionals
  • Software professionals
  • Graduates looking to build a career in Data Science and machine learning

All who are willing to work in machine learning or artificial intelligence and switch in the field of analytics along with the professionals, who deal with eCommerce, search, and other online consumer based organizations.

Prerequisites

The candidates willing to join the introduction to machine learning training should have a prior acquaintance on fundamentals of of programming & matrix algebra.

1. An Introduction

  • An Introduction to the Machine Learning
  • The Mathematical Prerequisites
  • An Introduction to the R

2. The Regression Algorithms

  • Linear Regression

3. The Classifiers: Bayesian and kNN

  • Bayesian classifiers

4. Tree Based Algorithms

  • The Decision Tress

5. SVM and Improving Performance

  • About SVM

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