A Pragmatic Approach to Machine Learning with R

A Pragmatic Approach to Machine Learning with R, available at $64.99, has an average rating of 5, with 14 lectures, based on 4 reviews, and has 43 subscribers.

You will learn about How to apply methodology to assure the success of machine learning projects. How to comfortably replace Excel with R, never returning to Excel for analysis again! How to import, shape, mould, filter, arrange and aggregate date in R instead of using a database for the same. How to make assertions about data with summary statistics and make one variable predictions. How to express domain experience given data and the overarching importance of Abstraction. How to make classifications with Logistic Regression, C5 Decision Trees and Neural Networks. How to make numeric predictions with Linear Regression, Regression Trees and Neural Networks. How to make pragmatic use of visualisations. How to use subjective probability and know its function in Bayesian networks. How to use Monte Carlo simulation with numeric predictions or classifications for the purposes of optimisation. How to integrate models as high performance HTTP microservices. This course is ideal for individuals who are Managers who want to lean more advanced analytical techniques and machine learning. or Data analysts who want to learn more advanced analytical techniques and machine learning. or Junior analysts who want to accelerate their career by taking on an advanced analytics or machine learning project on their own initiative. It is particularly useful for Managers who want to lean more advanced analytical techniques and machine learning. or Data analysts who want to learn more advanced analytical techniques and machine learning. or Junior analysts who want to accelerate their career by taking on an advanced analytics or machine learning project on their own initiative.

Enroll now: A Pragmatic Approach to Machine Learning with R

Summary

Title: A Pragmatic Approach to Machine Learning with R

Price: $64.99

Average Rating: 5

Number of Lectures: 14

Number of Published Lectures: 14

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: zł79.99

Quality Status: approved

Status: Live

What You Will Learn

How to apply methodology to assure the success of machine learning projects.
How to comfortably replace Excel with R, never returning to Excel for analysis again!
How to import, shape, mould, filter, arrange and aggregate date in R instead of using a database for the same.
How to make assertions about data with summary statistics and make one variable predictions.
How to express domain experience given data and the overarching importance of Abstraction.
How to make classifications with Logistic Regression, C5 Decision Trees and Neural Networks.
How to make numeric predictions with Linear Regression, Regression Trees and Neural Networks.
How to make pragmatic use of visualisations.
How to use subjective probability and know its function in Bayesian networks.
How to use Monte Carlo simulation with numeric predictions or classifications for the purposes of optimisation.
How to integrate models as high performance HTTP microservices.

Who Should Attend

Managers who want to lean more advanced analytical techniques and machine learning.
Data analysts who want to learn more advanced analytical techniques and machine learning.
Junior analysts who want to accelerate their career by taking on an advanced analytics or machine learning project on their own initiative.

Target Audiences

Managers who want to lean more advanced analytical techniques and machine learning.
Data analysts who want to learn more advanced analytical techniques and machine learning.
Junior analysts who want to accelerate their career by taking on an advanced analytics or machine learning project on their own initiative.

Data drives everything and machine learning is fast becoming the bedrock of all business processes. The future of work belongs to those who can create machine learning, not to those that simply use its output. There is a shortfall of individuals who can create machine learning. Get the skills you need for the future of work.

This is the same course, delivered by the same trainer, as shown in person to some of the world’s largest corporations. The instructor is available personally for questions and answers, via Udemy, with the same attention as if it were delivered in person.

This training course presents machine learning in natural business environments, cutting through the traditional academic applications to get to valuable business outcomes instead.

The course came about in a biographical fashion with the trainer documenting slides and procedures to reduce his skill fade after each machine learning consulting gig. Over time it grew to the following resources (which are published alongside the free modules):

Full training data was used throughout the course.
The full PDF book of training procedures.
The full PDF book of training slides used throughout the course.

R has become the standard for machine learning. The cost efficiency of R – it is free – coupled with the incredible variety of supplementary packages available to augment its powerful command-based interface, makes it a one-stop shop. It is little wonder that it is unseating commercial rivals as the corporate choice. When taken together with the wide array of packages available to R, nearly anything required of an individual practising machine learning, is available in R, and therefore, without any cost.

This course will develop your skills in data analysis, and summary statistics, as well as machine learning using regression, probability, decision trees, Naïve Bayesian networks and Neural Networks.

Course Curriculum

Chapter 1: Day 1

Lecture 1: The Methodology.

Lecture 2: Getting Started with R.

Lecture 3: Data Structures.

Lecture 4: Loading Shaping and Moulding Data

Lecture 5: Descriptive Statistics and Distribution Plots.

Chapter 2: Day 2

Lecture 1: Abstraction

Lecture 2: ggplot2 Rapid Exploration.

Lecture 3: Linear Regression.

Lecture 4: Logistic Regression and Classification.

Chapter 3: Day 3

Lecture 1: Splits, Decision Trees and Boosting.

Lecture 2: Naive Bayesian Networks.

Lecture 3: Neural Networks.

Lecture 4: Monte Carlo Optimisation.

Lecture 5: Plumber Microservices.

Instructors

Richard Churchman
A pragmatic approach to Machine Learning with R for 2021

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5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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