A Pragmatic Approach to Machine Learning with R
Learn how to apply methodology to ensure the success of machine learning projects and replace Excel with R for data analysis. This comprehensive course covers data manipulation, statistical analysis, classification, and prediction using various techniques. Suitable for managers, data analysts, and junior analysts looking to enhance their skills in advanced analytics and machine learning. Start building the skills needed for the future of work.
What you’ll 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.
Show moreShow less
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.
Who this course is for:
- 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.
User Reviews
Be the first to review “A Pragmatic Approach to Machine Learning with R”
You must be logged in to post a review.
There are no reviews yet.