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Fundamentals of Statistics and Machine Learning for Absolute Beginners

New – Learn to transform your regular applications with the power of statistics, analytics, and machine learning in this self-paced course.

GK# 7737

Course Overview

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This course will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand real-world examples that show the statistical side of Machine Learning and familiarize yourself with them. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, NumPy and so on. By the end of the course, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

Schedule

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What You'll Learn

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  • Learn the math and mechanics of Machine Learning via a developer-friendly approach
  • Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems
  • Offers practical solutions for simple linear regression and multi-linear regression
  • Compares logistic regression and random forest using examples
  • Introduces statistical terminology and machine learning
  • Get an overview of the most well-known and powerful tools, to solve computing problems using Machine Learning

Outline

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On-Demand Outline

Statistical terminology and machine learning

Linear regression

Logistic regression vs Random Forest

Introduction

The learning process

Clustering

Linear and Logistic Regression

Neural Networks

CNN

RNN

Recent models and developments

Who Should Attend

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This course will appeal to any developer who wants to learn the fundamentals of Machine Learning and statistics is and is keen to use them to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this course as an entry point.

Course Delivery

This course is available in the following formats:

On-Demand

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