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Machine Learning with R


Machine Learning with R


Advanced SQL
Analysing Data with Excel
Critical Thinking
Data Modelling with M and Power Query
Data Visualization with Python
Data Visualization with R
Excel Dashboards and Power Pivot
GIS ESSENTIALS – “Getting Spatially Enabled”
Introduction to Statistics
Machine Learning with Python
Microsoft Power BI Boot Camp
Microsoft Power BI Desktop Advanced Course
Microsoft Power Query
Microsoft PowerBI Dashboard User Course
MySQL Administration
PostgreSQL Administration
PostgreSQL for Business Intelligence and Mass-data-analysis
Problem Solving and Decision Making
Problem-Solving SAQA US242817
Python Dashboards with plotly Dash
Python Pandas and Jupyter ETL and Data wrangling
Python Regression
Python Statistical Modelling
Python Statistics
Python Web Apps with Flask MVC Framework
R, Into the Tidyverse (Data Wrangling and ETL)
Regression with R
Statistics with R


Mark Badham


Machine Learning is a branch of computer science that uses algorithms to help artificial intelligent systems learn and adapt. That learning process requires data and a programming language to process it. R is a language built for data science which has many packages to for machine learning, ready for you to use.

In this course you will learn:

  • Key concepts and terms for machine learning
  • To remove noise in data via smoothing
  • To assess the effectiveness of your model with cross-validation
  • To integrate model development using the Caret Package
  • Useful algorithms for important tasks
  • To apply machine learning to real-world applications
  • How to work with large data sets

Delivery Methods

Leading Training is focusing on providing virtual training courses for the foreseeable future and will only consider in-person and classroom training on request, with a required minimum group size of six delegates. We remain committed to offering training that is fast, focused and effective.

Delivery Method Duration Price (excl. VAT)
Classroom Days ZAR 0.00 Get a Quote
Live Virtual Training Days ZAR 0.00 Get a Quote

Discounts Available


Download Brochure

Information may change without notice.


  • Data scientists
  • Programmers
  • Business analysts 
  • Engineers
  • Scientists


Leading Training's Introduction to R Programming or equivalent knowledge

Course Outline / Curriculum

Introduction to machine learning

  • Notation
  • An example
  • Exercises
  • Evaluation metrics
    • Training and test sets
    • Overall accuracy
    • The confusion matrix
    • Sensitivity and specificity
    • Balanced accuracy and F1 score
    • Prevalence matters in practice
    • ROC and precision-recall curves
    • The loss function
  • Exercises
  • Conditional probabilities and expectations
    • Conditional probabilities
    • Conditional expectations
    • Conditional expectation minimizes squared loss function
  • Exercises
  • Case study: is it a 2 or a 7?


  • Bin smoothing
  • Kernels
  • Local weighted regression (loess)
    • Fitting parabolas
    • Beware of default smoothing parameters
  • Connecting smoothing to machine learning
  • Exercises

 Cross validation 

  • Motivation with k-nearest neighbors
    • Over-training
    • Over-smoothing
    • Picking the k in kNN
  • Mathematical description of cross validation
  • K-fold cross validation
  • Exercises
  • Bootstrap
  • Exercises

The caret package 

  • The caret train functon
  • Cross validation
  • Example: fitting with loess

Examples of algorithms 

  • Linear regression
    • The predict function
  • Exercises
  • Logistic regression
    • Generalized linear models
    • Logistic regression with more than one predictor
  • Exercises
  • k-nearest neighbors
  • Exercises
  • Generative models
    • Naive Bayes
    • Controlling prevalence
    • Quadratic discriminant analysis
    • Linear discriminant analysis
    • Connection to distance
  • Case study: more than three classes
  • Exercises
  • Classification and regression trees (CART)
    • The curse of dimensionality
    • CART motivation
    • Regression trees
    • Classification (decision) trees
  • Random forests
  • Exercises
  • Machine learning in practice 
  • Preprocessing
  • k-nearest neighbor and random forest
  • Variable importance
  • Visual assessments
  • Ensembles
  • Exercises
  • Large datasets 
    • Matrix algebra
    • Notation
    • Converting a vector to a matrix
    • Row and column summaries
    • apply
    • Filtering columns based on summaries
    • Indexing with matrices
    • Binarizing the data
    • Vectorization for matrices
    • Matrix algebra operations
  • Exercises
  • Distance
    • Euclidean distance
    • Distance in higher dimensions
    • Euclidean distance example
    • Predictor space
    • Distance between predictors
  • Exercises
  • Dimension reduction
    • Preserving distance
    • Linear transformations (advanced)
    • Orthogonal transformations (advanced)
    • Principal component analysis
    • Iris example
    • MNIST example
  • Exercises
  • Recommendation systems
    • Movielens data
    • Recommendation systems as a machine learning challenge
    • Loss function
    • A first model
    • Modeling movie effects
    • User effects
  • Exercises
  • Regularization
    • Motivation
    • Penalized least squares
    • Choosing the penalty terms
  • Exercises
  • Matrix factorization
    • Factors analysis
    • Connection to SVD and PCA
  • Exercises
  • 34 Clustering 
  • Hierarchical clustering
  • k-means
  • Heatmaps
  • Filtering features
  • Exercises

Schedule Dates and Booking

There are currently no scheduled dates.

Please note that this course needs a minimum of 6 delegates to schedule a course. You can choose to be added to the waiting list by clicking the button below, and we will contact you when we have enough delegates interested. Should we not get enough delegates, we will refund or credit your paid booking.

Add me to the waiting list

Should you need this course urgently, the following options are available:

  1. Pay for 6 delegates (whether you have them or not) and we will schedule the course as soon as possible.
  2. If you have fewer delegates and cannot pay for 6, we can negotiate a shortened course where some of the time will be spent in blended learning - watching videos and doing tutorials and exercises with some contact time with the trainer. We would want to discuss what your core needs are so that we cover those aspects. You need to have paid for 3 delegates at least.
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