Hands-on Machine Learning (2 DAYS)

GOTO Academy NL

Hands-on Machine Learning (2 DAYS)


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Machine Learning with Attila Houtkooper:

Topics Overview for Day 1 

Introduction to Machine Learning

  • We will go through the history of and major areas in Machine Learning so that you are able to explain many of the uses of Machine Learning.

    Python, Pandas, Numpy and SciKit Learn

    • We will ascert our common knowledge of these commonly used frameworks so that you are well-equipped to do the subsequent exercises.


      • We will use regression to predict future values of a continuous measure such as the price of honey and see how we can predict how it will change.


        • We will compare some classification algorithms to build a classifier on flower data. We look at the following algorithms: k-Nearest Neighbor, Decision Trees and Neural Networks.

          Unsupervised learning through Clustering and Principal Component Analysis

          • In discussing clustering we discover the usefulness of this technique in finding the structure in data without having any labels.

            Under- & overfitting

            • We will discuss the whys, dos and don’ts of splitting your data into training, test and validation sets and the use of cross-validation for comparing different model configurations.

              Data preprocessing and Embedding

              • Likely every learning model requires our data to be represented as numbers. That is more trivial when talking about a price level, and less when we want to include the description on the label in the data. We will go into representing text as vectors as well as scaling and transforming data to get the representation that is best for learning. 

                Topics Overview for Day 2

                 TensorFlow & Keras libraries

                • By working with TensorFlow we will get to know this modern framework built to create highly scalable computational graphs. We will be using Keras to build a neural network on top of this powerful engine as it provides an easy to use implementation of popular Neural Network architectures.

                  Convolutional Neural Networks

                  • We will be working with image data using Convolutional Neural Networks to show how we can use this powerful architecture to build a model that is independent of data variation.

                    Recurrent Neural Networks and Long-Short Term Memory

                    • We will use Long-Short Term Memory RNNs to work with variable-length datasets such as texts or event series.

                      Transfer Learning

                      • Why do all the training work ourselves when we can use pre-trained models? Transfer Learning is about using (parts of) pre-trained models to have a generic base to build a specialized model off of.

                        GPU servers on Amazon Web Services

                        • No Machine Learning professional can afford not to be aware of how to offload computationally intensive training to the cloud. We’ll dive into how to accomplish this using Amazon Web Services.

                          Guide on implementation within your organization

                          • We’ll take a deeper dive into what it takes to implement Machine Learning in your organization. Working with ML in actual work setting carries some important parts. Assessing data quality, getting the client to commit to the success of the project, choosing the right strategy, all help to execute a successful ML project.

                            Be thoughtfully introduced to the underlying theory before gaining hands-on ML experience.

                            Learn from the experts who apply machine learning techniques to real projects every day 


                            get in contact


                            2 days 0900-1700


                            Classroom @Trifork Amsterdam

                            Course delivered in English by Attila Houtkooper, Principal Machine Learning Architect at Trifork Amsterdam  


                            Developers who would like to take their ML projects to a real-world, enterprise level. Some experience with Python is assumed.

                            Testimonial about the teacher:

                            "Finally an awesome presentation on how to DO a project with ML"