Kıbrıs Uygulamalı Veri Bilimi Eğitimi

• GCSE Mathematics or above. Must be comfortable with mathematical and logical way of thinking
• Familiar with basic programming knowledge: variables, control flow, scope and functions
• Prior experience with Python or R will be an advantage

Bu eğitim, Makine Öğrenimi konusunda daha fazla bilgi sahibi olmak isteyen Makine Öğrenimi uygulayıcıları ve veri analistlerine yöneliktir.

At the end of this course attendees will know:

• Fundamental mathematics and statistics required for understanding Machine Learning
• The workings of some the most commonly used Machine Learning algorithms
• Cross validation techniques and the different metrics used to determine the quality of Machine Learning models built
• How to pick the best Machine Learning model for a given task

At the end of this course attendees will be able to:

• Perform data preparation using R or Python
• Build machine learning models using R or Python
• Perform simulation using R or Python
• Perform cross validation and evaluate a machine learning model

• Machine Learning: Method
• Introduction to Python
• Data Preparation in Python
• Machine Learning: Regression
• Introduction to Mathematics
• Mathematics of Machine Learning
• Machine Learning Algorithms
• Probability and Statistics
• Python Machine Learning
• Clustering in Python
• Decision Trees in Python
• Deep Learning in Python
• Evaluating ML algorithms
• Choosing ML algorithms: Cross Validation
• Ensemble Modelling