Lecture Notes on Machine Learning Algorithms

Introduction to Machine Learning

• Purpose: To prepare for data science interviews by understanding machine learning algorithms.
• Key distinctions in the field:
  • AI vs ML vs DL vs Data Science:
    • AI (Artificial Intelligence): Creating applications that perform tasks without human intervention.
    • ML (Machine Learning): A subset of AI that uses statistics to analyze data and make predictions.
    • DL (Deep Learning): A subset of ML that mimics human brain functions using multi-layered neural networks.
    • Data Science: Combines statistical analysis and machine learning to derive insights from data.

Types of Machine Learning

Supervised Learning

• Linear Regression: Understanding the math and geometric intuition.
• Key Metrics:
  • R-squared and Adjusted R-squared
  • Ridge and Lasso Regression

Unsupervised Learning

• Clustering: Understanding how to group similar data points (e.g., K-Means).
  • Different algorithms: K-means, hierarchical clustering.
  • Ridge: L2 regularization, helps prevent overfitting.
  • Lasso: L1 regularization, useful for feature selection.

Key Algorithms Discussed

1. Linear Regression:

   • Used to predict continuous outcomes.
   • Cost function: Mean Squared Error (MSE).

2. Ridge and Lasso Regression:

   • Ridge: Reduces overfitting by adding L2 penalty.
   • Lasso: Reduces overfitting and performs feature selection with L1 penalty.

3. Logistic Regression:

   • Used for binary classification.
   • Cost function based on log likelihood.

4. Decision Trees:

   • Can be used for both classification and regression.
   • Splits data based on feature conditions to create leaf nodes.

5. KNN (K-Nearest Neighbors):

   • Classification algorithm based on the closest training examples in the feature space.
   • Two distance metrics: Euclidean distance and Manhattan distance.

6. Random Forest:

   • An ensemble of decision trees to mitigate overfitting.
   • Works by averaging predictions from multiple trees.

7. Adaboost:

   • Combines weak learners (like shallow decision trees) into a strong learner.
   • Adjusts weights based on previous misclassifications.

8. SVM (Support Vector Machines):

   • Constructs hyperplanes in high-dimensional space to classify data.
   • Can handle both binary and multi-class problems.

9. DBSCAN:

   • Density-based clustering algorithm that separates outliers from clusters.
   • Identifies core points, border points, and noise points.

Key Concepts

• Bias-Variance Tradeoff: Understanding how to balance model complexity to minimize both bias and variance.

  • High bias: Model is too simple.
  • High variance: Model is too complex.

• Performance Metrics for Clustering:

  • Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters.

• Kernel Methods in SVM: Transitioning from linear to non-linear classification by mapping data into higher dimensions.

Practical Applications

• Practical sessions discussed using libraries like Scikit-learn.
• Hands-on examples with datasets for each algorithm.

Conclusion

• The lecture provided an in-depth look at various machine learning techniques, algorithms, and their applications, preparing students for data science roles.