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Machine Learning with Python

Welcome to my repository designed to introduce and teach Machine Learning (ML) using Python, focusing on both regression and classification models. This resource is crafted to be a comprehensive guide for learners at all levels who are interested in understanding and applying fundamental ML techniques in their projects.

Topics Covered

📈 Regression Models

  • Introduction to Regression: Understand the concept, purpose, and types of regression in ML.
  • Regression Techniques: Dive into various regression models including Linear Regression, Ridge, Lasso, ElasticNet, Support Vector Regression (SVR), Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor.

📊 Classification Models

  • Introduction to Classification: Learn about the basics of classification, its significance, and its applications in ML.
  • Classification Techniques: Explore different classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest, and Gradient Boosting Machines (GBM).

Common Topics for Both

  • Data Preprocessing: Techniques for loading, understanding, and preparing data.
  • Modeling Approach: Insights into the modeling process, from selecting models to tuning parameters.
  • Evaluation Metrics: Discussion on various metrics like MAE, MSE, RMSE, R-Squared for regression, and accuracy, precision, recall, F1 score for classification.
  • Model Optimization: An overview of optimization techniques including Grid Search and Random Search for model selection.

Activities Included

  • Practical Exercises: Step-by-step guides for data loading, preprocessing, model training, prediction, and evaluation.
  • Theoretical Foundations: Provides a deeper understanding of each ML concept through concise theoretical insights.
  • Data Visualization: Demonstrates techniques for visualizing data, model predictions, and performance metrics effectively.

Upcoming Contents

  • Unsupervised Learning Models: We plan to include topics on clustering and dimensionality reduction techniques.
  • Advanced ML Topics: Future updates will delve into neural networks, deep learning, and reinforcement learning to cater to advanced learners.

Stay tuned for more, and happy learning!