Data Science & Machine Learning Cheat Sheet
1. NumPy
2. Pandas
Source — https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/#gs.KQOXkLU
Source — https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.lfJBxWo
3. Scipy
4. Matplotlib
5. Scikit-learn
6. Neural Networks Zoo
7. PySpark
8. R Studio (dplyr and tidyr)
9. Keras
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DeleteData Cleaning: Techniques for handling missing values, outliers, and inconsistencies in data. (e.g., imputation, normalization, anomaly detection)
Exploratory Data Analysis (EDA): Summarizing and visualizing data to understand its characteristics and relationships between variables. (e.g., histograms, scatter plots, correlation analysis)
Feature Engineering: Creating new features from existing data to improve model performance. (e.g., feature scaling, dimensionality reduction)
Model Selection and Evaluation: Choosing the right type of model for your problem and evaluating its performance on unseen data. (e.g., train-test split, cross-validation, metrics like accuracy, precision, recall)
Machine Learning
Supervised Learning: Learning a model from labeled data to predict a target variable.
Regression: Predicting continuous values (e.g., linear regression, decision trees)
Classification: Predicting discrete categories (e.g., logistic regression, support vector machines)
Unsupervised Learning: Learning patterns from unlabeled data without a predefined target variable. (e.g., k-means clustering, dimensionality reduction techniques like PCA)
Model Training: The process of fitting a model to the training data by adjusting its parameters. (e.g., gradient descent)
Overfitting and Underfitting: Balancing model complexity to avoid overfitting to the training data (memorizing noise) or underfitting (failing to capture the underlying pattern).
Regularization: Techniques to prevent overfitting by penalizing model complexity. (e.g., L1/L2 regularization)
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