NOTICE
We are open for classroom training with strict Covid-19 Level 1 protocols in place.
Details
Artificial Intelligence may be our generation's most exciting pursuit, and, in order for a machine to be intelligent, it must be able to learn. Machine learning is the process by which computer systems access data, run experiments, and learn from experience, much the same way we do. In this course, you will learn how to set up these systems and how they might benefit your business.
This dynamic course covers both theoretical explanations and practical activities, which comprise over 60% of the course. The main goal is that participants face “real” problems, experience consequential challenges and learn to come up with a solution – guided by the instructor at all stages of the process.
Delivery Methods
| Delivery Method | Duration | Price (excl. VAT) | |
|---|---|---|---|
| Fulltime | 5 Days | R 34,000.00 | |
| Webinar | 5 Days | R 34,000.00 |
Discounts Available
Save up to 10% by booking and paying 10 business days before the course.
Brochure:
Audience
This course is intended for technical analysts or mid-level managers who are willing to do their first steps in Machine Learning.
Pre-Requisites
Analysts/technical managers with at least 1 year of programming experience (Ideally, also experience in Python)
Course Outline / Curriculum
Day 1:
Machine Learning: Introduction and explanation of main concepts.
About Python/Jupyter
Overview of main Python libraries to be used
Exploratory Data Analysis: in theory
Exploratory Data Analysis: in practice
Day 2:
Supervised Learning: Introduction and explanation of main concepts.
- Explanation of Training Process: Training/Validation/Testing, Cross-validation
- Recap: Regression vs Classification
- Cost/Loss functions
- What is done in training? Minimization of Loss Function. Example Algorithms
- Performance Evaluation
- Steps for successful Model building
Regression
Day 3:
Classification
- Classification Algorithms
- Ensemble Algorithms
- Performance Evaluation
Day 4:
Unsupervised Learning
- Unsupervised Algorithms
- Ensemble Algorithms
- Performance Evaluation
From lab to production: challenges and common problems
The importance of distributed computing in Machine Learning
Day 5:
Introduction to neural networks
- Definition
- Main Concepts
- Tensorflow Playground demo
- Activation Functions
- NN training process
- Multi-class Problems and Softmax
- Convolutional NN
- Keras Demo and Explanation of Keras Library
- Transfer Learning
- Hyperparameter Tuning
Deep Learning: What is it and where can it be applied?
Machine Learning in my organization: How can I implement ML considering the current problems we face?
Final summary, questions, suggestions, etc.
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.
Should you need this course urgently, the following options are available:
- Pay for 6 delegates (whether you have them or not) and we will schedule the course as soon as possible.
- 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.