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Open Recruitment Calon Admin KCV | materials README

This repository contains materials and information for a comprehensive Python and Machine Learning course. The course is divided into four sessions, each covering essential topics and tools in Python programming, data manipulation, visualization, and machine learning.

Session Details

Session 1: Python Basics and Python Libraries for Data

  • Speaker: Anneu Tsabita
  • Topics Covered:
    • Python installation
    • Python interpreter usage
    • Introduction to basic Python concepts
    • Introduction to Python libraries for data manipulation and analysis such as NumPy, Pandas, Matplotlib, and Seaborn.

Session 2: Basic Machine Learning, Data Cleaning, and EDA

  • Speaker: Rule Lulu
  • Topics Covered:
    • Basic introduction to Machine Learning
    • Techniques for data cleaning
    • Data preprocessing
    • Data visualization
    • Exploratory Data Analysis (EDA)
    • Direct implementation of the concepts taught

Session 3: Python Libraries for Machine Learning

  • Speaker: Darren Prasetya
  • Topics Covered:
    • Utilization of Python libraries for Machine Learning such as Scikit-learn
    • Introduction to various Machine Learning algorithms
    • Application of supervised regression and classification using Scikit-learn

Session 4: Tuning, Validation, and Kaggle

  • Speaker: Andrian
  • Topics Covered:
    • Model tuning in Machine Learning
    • Model validation
    • Introduction to Kaggle competitions and related practical exercises

Requirements

  • Python 3.x
  • Jupyter Notebook or any Python IDE

Contact Information

For any questions or inquiries regarding the course, please contact:

Feel free to reach out to the respective speakers or administrators for assistance or further information.