Machine Learning


5 Days

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Course Outline

Understanding the basics of artificial intelligence and machine learning. During the course we will get to know and apply the development cycle of data science (information gathering, information analysis, information preparation, model building, training, analysis of results),

we will learn the leading algorithms in Machine Learning - and we will understand when to use each algorithm and different control processes.




Machine Learning Introduction
  • Introduction to ML
  • Bias-Variance tradeoff
  • Types of ML
  • CRISP-DM methodology - the typical work cycle of the Machine Learning project
  • Managing Data science projects
  • Methods for evaluation.
  • Monitoring ML models
Exploratory data analysis (EDA)
  • Data representation types: Scatters, pie, column diagram
  • Cleaning the data
  • Data completion, Data normalization and scaling
  • Feature selection: forward and backward selection
  • Feature extraction
  • Balancing the data
ML algorithms
  • Supervised learning algorithms (Random Forest, Knn etc.)
  • Unsupervised learning algorithms


  • Knowledge of Python is required

Upcoming Meetings

The course will present basic concepts and algorithms require to communicate in a data-driven environment"
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Target Audience

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    • Israel
    • Poland
    • USA
    • India
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