Machine Learning Cheatsheet

Machine Learning Cheatsheet

From beginner to advanced concepts explained in plain language

ML Fundamentals

Core concepts every beginner should know

  • What is Machine Learning? Beginner

    Teaching computers to learn patterns from data without being explicitly programmed for each task.

  • Supervised Learning Beginner

    Learning from labeled examples. Like showing a child many pictures of cats and dogs with labels, so they learn to tell them apart.

  • Unsupervised Learning Beginner

    Finding patterns in data without labels. Like grouping similar songs together without knowing their genres.

  • Training & Testing Beginner

    Training is learning from examples, testing is checking how well the model performs on new, unseen data.

Key Algorithms

Essential ML algorithms explained simply

  • Linear Regression Beginner

    Drawing a straight line through data points to predict numerical values. Like estimating house prices based on size.

  • Decision Trees Beginner

    Making predictions by asking a series of yes/no questions. Like a flowchart for classification.

  • K-Means Clustering Intermediate

    Grouping similar data points together. Like automatically organizing photos into categories.

  • Neural Networks Intermediate

    Inspired by the human brain, these networks learn complex patterns through interconnected nodes.

Model Evaluation

How to measure ML model performance

  • Accuracy Beginner

    What percentage of predictions were correct. Good starting point but can be misleading with imbalanced data.

  • Precision & Recall Intermediate

    Precision: How many selected items were relevant. Recall: How many relevant items were selected.

  • Cross-Validation Intermediate

    Testing your model on multiple subsets of data to ensure it works well in different scenarios.

  • ROC Curve Advanced

    A graph showing how well your model distinguishes between classes at different threshold settings.

Data Preparation

Getting your data ready for ML

  • Feature Engineering Intermediate

    Creating new input features from existing data to help algorithms learn better patterns.

  • Handling Missing Values Intermediate

    Dealing with incomplete data by filling in reasonable values or removing problematic records.

  • Normalization Intermediate

    Scaling numerical data to a standard range so no single feature dominates the learning process.

  • Data Leakage Advanced

    When information from outside the training data sneaks into the model, creating overly optimistic performance estimates.

Advanced Concepts

Next-level machine learning topics

  • Deep Learning Advanced

    Using multi-layered neural networks to learn complex patterns from large amounts of data.

  • Transfer Learning Advanced

    Using knowledge gained from solving one problem to help solve a different but related problem.

  • Ensemble Methods Advanced

    Combining multiple models to get better performance than any single model could achieve alone.

  • Reinforcement Learning Advanced

    Learning through trial and error by receiving rewards for good actions and penalties for bad ones.

Practical Tips

Best practices for real-world ML

  • Start Simple Beginner

    Always begin with a simple model before trying complex algorithms. You might be surprised how well they work.

  • More Data Beats Fancy Algorithms Intermediate

    Often, having more quality data gives better results than using a more sophisticated model.

  • Watch for Overfitting Intermediate

    When your model learns the training data too well but fails on new data. Like memorizing answers instead of understanding concepts.

  • Interpretability Matters Advanced

    Sometimes a simpler model that you can explain is better than a black box that performs slightly better.

Machine Learning Cheatsheet | Learn, Practice, Build

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