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Machine Learning with Python


Machine Learning with Python


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 R
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 subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for specific tasks.

In essence, machine learning algorithms use patterns and insights discovered in data to improve their performance over time, without the need for human intervention. This course covers the essential components to machine learning using Python, over 10 days.

This 10-day course covers an overview of machine learning concepts and types, including supervised and unsupervised learning, with hands-on implementation using Python libraries like Scikit-learn. It includes techniques for evaluating and validating models, interpreting metrics, and improving performance, followed by an exploration of unsupervised learning algorithms and model selection strategies.

If there is time, advanced topics such as ensemble learning and optional deep learning concepts will be introduced. However, we do aim for practical application and will cover deployment techniques for deploying machine learning models in production environments.

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 10 Days ZAR 29,500.00 Get a Quote
Live Virtual Training 10 Days ZAR 24,500.00 Get a Quote

Discounts Available

Save up to 10% by booking and paying 10 business days before the course.


Download Brochure

Information may change without notice.


  • Data scientists
  • Data analysts
  • Business Intelligence professionals
  • Software Engineers
  • Business Professionals
  • Researchers
  • Start-up owners
  • Anyone interested in machine learning



  • Python programming proficiency
  • Core Mathematics to Grade 12
  • Data analysis skills
  • Critical thinking and problem-solving skills

Course Outline / Curriculum

Introduction to Machine Learning

  • Overview of machine learning concepts, types of machine learning (supervised, unsupervised, semi-supervised, reinforcement learning), and applications. 

Supervised Learning

  • Introduction to supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and k-nearest neighbours (k-NN). 
  • Hands-on implementation of supervised learning algorithms using Python libraries such as Scikit-learn. 

Model Evaluation and Validation

  • Techniques for evaluating and validating machine learning models, including cross-validation, train-test split, and performance metrics (accuracy, precision, recall, F1-score, ROC AUC). 
  • Interpretation of evaluation metrics and techniques for improving model performance. 

Unsupervised Learning

  • Introduction to unsupervised learning algorithms, including clustering (k-means, hierarchical clustering), dimensionality reduction (PCA, t-SNE), and association rule learning. 
  • Hands-on implementation of unsupervised learning algorithms using Python libraries. 

Model Selection and Tuning

  • Strategies for model selection, hyperparameter tuning, and optimization techniques such as grid search and random search. 
  • Techniques for avoiding overfitting and underfitting, including regularization and model complexity control. 

Ensemble Learning

  • Explanation of ensemble learning methods, including bagging, boosting, and stacking. 
  • Implementation of ensemble learning algorithms such as Random Forest and Gradient Boosting. 

Deep Learning (Optional)

  • Introduction to deep learning concepts, including artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and deep learning frameworks (TensorFlow, Keras, PyTorch). 
  • Hands-on implementation of deep learning models for tasks such as image classification, natural language processing (NLP), and sequence prediction. 

Deployment and productionization


  • Techniques for deploying machine learning models in production environments, including containerization (Docker), model serving (Flask, FastAPI), and monitoring. 

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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