[TRAINING] Machine Learning in 3 days: Munich

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[TRAINING] Machine Learning in 3 days: Munich
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From Mon 28 October 2019 to Wed 30 October 2019
From Monday 28 October 2019 to Wednesday 30 October 2019
9:00 AM - 5:00 PM

Why this training?

This 3-day course will give you a comprehensive overview of various tools, frameworks, and concepts behind machine learning.

In just three days, you will get a clear understanding of the core machine learning techniques, mathematical concepts, and engineering solutions for daily usage. You will go through the complete process of building machine learning systems, from data understanding to modelling.

During hands-on labs, accompanying each theoretical unit, you will see the inner workings of a machine learning model and will reproduce the stages of its development life cycle.

At the end of the course, the participants will be able to design working scripts that can be used as a basis for creating algorithms to address business-specific challenges.

Who should attend?

This training is a good fit for anyone who has a basic understanding of Python and wants to acquire new skills in just 3 days

Course objectives

  • Gain a basic understanding of machine learning concepts

  • Learn how to use main troubleshooting techniques of machine learning

  • See the inner workings of a machine learning model and reproduce the stages of its development life cycle


Day 1. Core Concepts and Techniques


  • An introduction to machine learning tasks and definitions

  • Core principles of building machine learning algorithms

  • A diversity of machine learning algorithms: from linear regression to random forest

  • Core Python packages for machine learning


  • Linear and logistic regressions

  • k-nearest neighbors and k-means

  • Decision trees and random forest

  • Handling classification, regression, and clustering tasks

*Packages of choice are Pandas/NumPy/scikit-learn

Day 2. Advanced Algorithms


Day 2 will cover the use of advanced theoretical concepts underlying such complex models as:

  • LASSO/Ridge (regularization)

  • PCA/SVD (dimensionality reduction)

  • Advanced clustering algorithms, such as DBSCAN, expectation-maximization (different similarity approaches to data)

  • Naive Bayes (The Bayes theorem)

  • Complex ensembling schemes, gradient boosting, stacking (iterative refinement)

  • Algorithmic hyperparameter tuning



  • PCA

  • DBSCAN, expectation-maximization, agglomerative clustering, mean shift

  • Naive Bayes

  • Gradient boosting machine, stacking

  • Tree-structured Parzen estimator

*Packages of choice are Pandas/NumPy/scikit-learn/HyperOpt/XGBoost

Day 3. Feature Engineering and Development Methodology


A wide range of topics related to building ML models will be covered:

  • Feature engineering

  • Dealing with missing data and outliers

  • Dealing with imbalanced classification

  • Advanced validation schemes

  • Handling of model versioning

  • CRISP-DM as a major machine learning development methodology


Feature engineering:

  • Polynomial and logarithmic features, combinations of features

  • Periodic feature encoding

  • Target encodings

Imbalanced classification:

  • Advanced metrics for classification

  • Threshold tuning

  • Over- and undersampling (SMOTE)

Missing data handling:

  • Imputation of missing values using k-nearest neighbors or decision trees

Advanced validation:

  • Cross-validation for time series

*Packages of choice are Pandas/NumPy/scikit-learn

After participating in the course you will get a certificate of completion!


Altoros recommends that all students have:

- Basic Python programming skills, a capability to work effectively with data structures

- Experience with the Jupyter Notebook applications

- Basic experience with Git

- A basic understanding of matrix vector operations and notation

- Basic knowledge of statistics

- Basic knowledge of command line operations

All code will be written in Python with the use of the following libraries:

- Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one.

- scikit-learn

- Matplotlib

All these libraries will be installed using Anaconda.

Requirements for the workstation:

- A web browser (Chrome/Firefox)

- Internet connection

- A firewall allowing outgoing connections on TCP ports 80 and 443

The following developer utilities should be installed:

- Anaconda

- Jupyter Notebook (will be installed using Anaconda)

If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

Payment info:

If you would like to get an invoice for your company to pay for this training, please email to training@altoros.com and provide us with the following info:

  • Name of your Company/Division which you would like to be invoiced;

  • Name of the person the invoice should be addressed to;

  • Mailing address;

  • Purchase order # to put on the invoice (if required by your company).

Please note our classes are contingent upon having 7 attendees. If we don't have enough tickets sold, we will cancel the training and refund your money one week prior to the training.Thanks for the understanding.

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