Core Machine Learning Concepts

This repository outlines essential machine learning concepts and provides a list of resources to help you learn more about each topic.

1. Supervised Learning

Description:
Learn models from labeled training data to make predictions.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Machine Learning" by Tom Mitchell
  • "Deep Learning" by Ian Goodfellow et al.
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

Subtopics:

• Regression
• Classification

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2. Unsupervised Learning

Description:
Learn patterns from unlabeled data.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Machine Learning" by Tom Mitchell
  • "Deep Learning" by Ian Goodfellow et al.
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

Subtopics:

• Clustering
• Dimensionality Reduction

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3. Reinforcement Learning

Description:
Learn through trial and error, interacting with an environment.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

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4. Neural Networks

Description:
Learn from data using interconnected layers of artificial neurons.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Deep Learning" by Ian Goodfellow et al.
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

Subtopics:

• Artificial neurons
• Layers
• Backpropagation

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5. Common Machine Learning Algorithms

Description:
Popular algorithms used in machine learning.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Machine Learning" by Tom Mitchell
  • "Deep Learning" by Ian Goodfellow et al.
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

Subtopics:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines (SVMs)
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Principal Component Analysis (PCA)
• K-Means Clustering

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6. Evaluation Metrics

Description:
Metrics used to assess the performance of machine learning models.

Preparation Resources:

• Online Courses:
  • Coursera
  • edX
  • Udacity
  • Fast.ai
• Textbooks:
  • "Machine Learning" by Tom Mitchell
  • "Deep Learning" by Ian Goodfellow et al.
• Research Papers:
  • arXiv
  • Google Scholar
• Online Communities:
  • Stack Overflow
  • Kaggle
  • Reddit

Subtopics:

• Accuracy
• Precision
• Recall
• F1-score
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)