Data Science, Machine Learning, and Data Analysis

Key Topics Covered

• Supervised Learning: Classification and Regression
• Unsupervised Learning: Clustering and Dimensional Reduction
• Semi-supervised Learning
• Supervised Learning vs. Unsupervised Learning
  • Classification examples: email spam detection
  • Regression examples: predicting continuous values
• Mathematical Formulas and Concepts
  • Linear regression: y = mx + b
  • Probability Basics
  • Nearest Neighbors
• Machine Learning Issues
  • Underfitting and Overfitting
  • Validation and Regularization
• Ensemble Methods
  • Bagging, Decision Trees, Random Forests
• Support Vector Machines (SVM)
• Naive Bayes and Probabilistic Models
  • Posterior and Likelihood Probabilities
• Data Processing with NumPy and Pandas
  • Data manipulation, indexing
  • Splitting datasets, scaling variables
• Feature Scaling
  • Normalization
  • Standardization (Z-Score)
• Data Visualization
  • Outliers
• Performance Metrics for Classification
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Mean Absolute Error
  • Mean Squared Error
• Dimensionality Reduction
  • Principal Component Analysis (PCA)

Supervised Learning

• Classification
  • Examples include email spam detection.
  • Features (X): number of words, keywords.
  • Labels (y): spam or not spam.
• Regression
  • Examples include predicting numeric values (e.g., prices).
  • Linear regression formula: y = mx + b.
  • Concepts: independent variables (X), dependent variable (y).

Unsupervised Learning

• Clustering
• Dimensional Reduction: Simplifying datasets while retaining important information.

Important Concepts in Detail

• Linear Regression
  • Simple linear equation y = mx + b.
• Probability Basics
  • Posterior probability P(A|B)
  • Likelihood P(B|A)
  • Prior probability P(A)

Nearest Neighbors

• Calculating distances between data points.

Common Problems in Machine Learning

• Underfitting: Model is too simple.
• Overfitting: Model is too complex.
• Regularization: Technique to prevent overfitting.

Ensemble Methods

• Bagging: Bootstrapping and aggregation.
  • Bootstrap samples with replacement.
• Random Forests: Multiple decision trees.
• Support Vector Machines (SVM): Hyperplanes and classification.

Naive Bayes Classifier

• Probability Formulas
  • Posterior probability: P(A|B) = (P(B|A) * P(A)) / P(B)*

Data Processing

• NumPy and Pandas
  • Creating arrays, data manipulation.
  • Example: np.array(), pd.DataFrame().
  • Handling missing data, replacing values.

Feature Scaling

• Normalization: Rescaling to [0, 1].
  • Example formula: (X - min) / (max - min).
• Standardization (Z-Score)
  • Formula: (X - mean) / standard deviation.

Data Visualization

• Outliers: Detecting and managing outliers.

Performance Metrics for Classification

• Accuracy: Correct predictions / Total predictions.
• Precision: True Positives / (True Positives + False Positives).
• Recall: True Positives / (True Positives + False Negatives).
• F1 Score: 2 * (Precision * Recall) / (Precision + Recall).
• Mean Absolute Error (MAE): Average of absolute differences between predicted and actual values.
• Mean Squared Error (MSE): Average of squared differences between predicted and actual values.

Dimensionality Reduction

• Principal Component Analysis (PCA)
  • Transforming correlated features into uncorrelated components.
  • Explained variance by components.