Machine Learning Projects

This repository contains machine learning projects and examples implemented in Python.

SDAIA ML Bootcamp

The SDAIA ML Bootcamp folder in this repository contains materials related to the SDAIA Machine Learning Bootcamp. It includes lecture notes, code samples, and project files from the bootcamp sessions. you can find the content of the bootcamp in each week.

• Week01:
  1. Data science life cycle and stages.
  2. Data processing techniques for data pre-processing.
  3. EDA and the use of visualization.
  4. Different methods and platforms for obtaining data.
  5. Different types of data and their properties.
  6. Essential data science skills for subsequent modules and projects.

• week02:
  1. Machine learning principles and applications.
  2. Supervised learning techniques.
  3. The K-algorithm and its applications.
  4. Linear regression modeling of relationships.
  5. Naive Bayes for text classification and application.
  6. Logistic regression for binary classification tasks.

• week03:
  1. Classification techniques and their applications.
  2. Naive Bayes for text classification and spam filtering.
  3. Split criteria and deal with different types of variables.
  4. Transformations for Nonlinear Classification.
  5. Unsupervised learning techniques.
  6. Interpret clustering algorithms for grouping similar data.

• week04:
  1. Techniques and metrics for evaluating machine learning models.
  2. Common data-related challenges in machine learning.
  3. Statistical models validated and tested.
  4. Analyze and diagnose model performance issues and apply appropriate solutions.
  5. Advanced techniques to improve the model.