Introduction to Machine Learning
Dates & Duration
See available dates for this course!
If there is no date and time available for a certain training you are interested in, please contact us.
This training can also be held at your place or as online training especially for your company. In this case the course agenda can also be adapted to suit your needs best.
Learn more about our customized training >>
This course takes 3 days with 8 hours each (on site) or 5 hours each (online).
The only difference is that there will be less practical exercises in an online course. However, we will hand them over to you and you can still do them on your own and ask our consultants for feedback or help if needed.
Course description
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.
Audience
Analysts/technical managers with at least 1 year of programming experience (Ideally, also experience in Python)
Course outline
Below you can find the topics that can be covered in this course. The actual choice of topics depends on the needs and interests of the course participants.
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
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
Classification
- Classification Algorithms
- Ensemble Algorithms
- Performance Evaluation
Unsupervised Learning
- Unsupervised Algorithms
- Ensemble Algorithms
- Performance Evaluation
From lab to production: challenges and common problems
The importance of distributed computing in Machine Learning
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?
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