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Learn The Latest Engineering features For Machines 2020

If you want to learn the latest Engineering feature for machines, then this tutorial is for you. Our world today is technologically driven, you can agree with me that in no distant time, things won’t be done manually again. Machines will take over.

This is an opportunity for you to learn the latest engineering feature for machines and also new engineering features for machines, so you can be updated with the latest machine technologies.

How about you leverage on this great opportunity to transform variables into your data and build more powerful machine learning models.

The latest Engineering features For machines will teach you how to design so many machines that can be used to solve so many life problems.

The benefit of the Course

  • You will learn different techniques for missing data credits
  • Turn categorical variables into numbers as you gather important information
  • Learn how to deal with unusual, rare and invisible categories
  • Convert distorted variables to Gaussian
  • Converts numeric variables to discrete
  • Remove outliers from your variables
  • Extract important resources from data and time variables
  • Understand the techniques used in organizations worldwide and in data competitions
  • Expand your repertoire of techniques for preprocessing data and creating more powerful models for machine learning.

YOU WILL LEARN HOW TO

  • Assume your missing data
  • Code your categorical variables
  • Transform your numeric variables to meet the assumptions of the ML model
  • Convert your numeric variables to discrete areas
  • Remove outliers
  • Dealing with data and time variables
  • How to work with different time zones
  • Dealing with mixed variables that contain strings and numbers

Requirements

  • A python installation
  • Jupyter notebook installation
  • Python coding skills
  • Some experiences with NumPy and pandas
  • Familiarity with machine learning algorithms
  • Familiarity with Scikit-Learn

Who Is This Course Meant For

  • Data Analyst
  • An individual with a knowledge of the commonly prediction tools
  • Someone who has perhaps built a linear regression or a classification tree model
  • Raw Data scientists who want to start preprocessing data sets to create machine learning models
  • Scientists who want to learn more about machine learning techniques
  • A data scientist who wants to improve their coding skills and program best practices for resource engineering
  • Software engineers, mathematicians, and academics switch from careers to data science
  • Analysts who want to experiment with various resource engineering techniques in data competitions
  • Software developers who want to learn how to use Scikit-learn and other open-source resource development packages

How to Download the File

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After installation, upload the file and start downloading the file. The file is up to  3.76 GB worth of videos. Start a full school

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