https://github.com/alejandrolara11/machinelearningcourse
Machine Learning Basics: From Setup to Clustering
https://github.com/alejandrolara11/machinelearningcourse
data-analysis data-science machine-learning numpy pandas plotly preprocessing-data python scikit-learn seaborn streamlit
Last synced: 16 days ago
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Machine Learning Basics: From Setup to Clustering
- Host: GitHub
- URL: https://github.com/alejandrolara11/machinelearningcourse
- Owner: AlejandroLara11
- Created: 2024-11-29T22:48:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-09T22:41:05.000Z (over 1 year ago)
- Last Synced: 2025-02-01T01:30:49.905Z (about 1 year ago)
- Topics: data-analysis, data-science, machine-learning, numpy, pandas, plotly, preprocessing-data, python, scikit-learn, seaborn, streamlit
- Language: Python
- Homepage:
- Size: 1.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MachineLearningCourse
Machine Learning Basics: From Setup to Clustering
Welcome to my repository for the "Machine Learning Basics" course! 🚀 This repository documents my journey into the world of Machine Learning, covering foundational concepts, practical data handling, and essential algorithms.
📚 Course Content
Introduction to Machine Learning:
What is Machine Learning?
Understanding its applications and types (supervised, unsupervised).
Environment Setup:
Setting up a virtual environment for ML projects.
Data Preprocessing:
Beginner-friendly techniques for data treatment.
Improving workflows with pipelines.
Data Collection and Exploration:
Techniques to gather and explore datasets.
Handling missing values and outliers.
Normalization and Encoding:
Scaling data for better model performance.
Encoding categorical variables for ML algorithms.
Training and Testing:
Splitting datasets into training and testing sets.
Understanding the importance of cross-validation.
Model Evaluation:
Metrics for classification (e.g., precision, recall, F1-score).
Metrics for regression (e.g., RMSE, R-squared).
Clustering Algorithms:
K-Means clustering.
Hierarchical clustering and dendrograms.
💻 What’s in this Repository?
Code Implementations: Step-by-step Python notebooks for each topic.
Hands-On Practice: Exercises and challenges to reinforce learning.
Mini-Projects: Practical examples to apply concepts to real-world scenarios.
Notes: Concise summaries of theoretical and practical lessons.
🛠️ Technologies and Libraries
Python 🐍
Libraries: numpy, pandas, matplotlib, scikit-learn, seaborn, streamlit, plotly.
🌟 Objectives
Develop a solid understanding of ML concepts and workflows.
Learn essential data preprocessing techniques.
Gain hands-on experience with clustering and evaluation metrics.
Build confidence to explore more advanced Machine Learning topics.