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Leading Training is open for classroom based training as from the 10th of June. We have put strict safety measures in place, please see our COVID-19 READINESS PLAN
Those not comfortable attending classroom training are welcome to attend via webinar.
Details
This course is intended for technical analysts or mid-level managers who are willing to do their first steps in Machine Learning. The dynamic of this course contemplates both theoretical content explanation and practical activities, with a special focus on the last part (60-65% 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 this process.
The dynamic of this course contemplates both theoretical content explanation and practical activities, with a special focus on the last part (60-65% 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 this process.
Delivery Methods
| Delivery Method | Duration | Price (excl. VAT) | |
|---|---|---|---|
| Fulltime | 5 Days | R 34,000.00 | |
| Webinar | 10 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
To apply for a booking, click on the relevant "Book Now" button below.
Note: places are only fully secured once payment has been made.
| Start Date | Branch | Status | |
|---|---|---|---|
| Mon 31 Aug 2020 | Not Applicable | Scheduled |