Info: 2 day course

When: 06.12.2018

Where: Bucharest, Romania

Advanced Machine Learning; Scientific Python and Machine Learning- By Dr. Dany Livne

Course Overview:

Data scientists use algorithms and frameworks which enables computers to solve problems that are classified on a higher complexity level than traditional algorithms. Probably the most successful frameworks, gaining high acceptance both in academy and by major businesses is the Python open source language. First released in 1989, Python is a fast, object oriented, portable, scientific, enterprise, back-end and front end application development framework. Focusing on readability and fast deployment, it is the ideal tool for the modern data scientist.
In this course we will introduce the main building blocks of the language, relevant for the data scientist; its most important libraries such as NumPy, Pandas and Scikit-learn, as well as its newest additions around data presentation and parallelism.
We will review various use cases and implement mini-labs in Python.


Understand the different tools available for the data scientist in Python, best practices and design patterns


One to two years programming skills in any other languages, and the introduction to machine learning basic course.

Lecturer: Dani Livne

Mr. Dani Livne is a lecturer on topics of algorithms and data science at Logtel.

Dani holds an MBA and a Mathematics MCs. Degrees from Tel-Aviv university and is still collaborating with various academies in Israel, either teaching or participating in math’s seminars.

Some of Dani’s past roles in various high tech companies include senior development, leading development teams and serving as a software architect. As part of these tasks Dani relocated to

New-York for three years where he gained extensive experience with the American Telco market.

In his recent position Dani served as an architect in a large cyber company and now he is a Data Scientist in a company that does retail marketing personalization.

Day 1

1. Introduction to Python

Development environment

Basic constructs, functions, scopes, classes and objects, main collections

NumPy and Pandas

Developing machine learning algorithms in Python

Validation in Python

Parallel distribution in Python

2. Scikit-learn library and tools  


Correlation, feature selection and reduction

Model selection

Linear models

Day 2

3. Algorithms

Clustering and classification

Trees and SVM


4. Advanced Topics       

Plotting results


Deep learning in Python

Parallel distribution using Dask

Lab – recommendation system

5. Summary   


Early Bird

645 Euro

cost total per participant

Full Price

860 Euro

cost total per participant

Groups larger than 10 participants



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