Machine Learning Syllabus & Notes
Supervised and unsupervised learning, regression, classification algorithms, decision trees, neural networks, clustering, and model evaluation metrics.
Topics in Machine Learning
- K-Means & Clustering in Unsupervised Learning: Master K-Means clustering, centroid initialization, Euclidean distance assignment, centroid updating, the Elbow Method for optimal K, and key limitations in Unsupervised Learning.
- Machine Learning & Supervised Learning Fundamentals: High-yield concepts, mathematical formulas, and practice questions for Machine Learning and Supervised Learning.
- Recommendation Systems, Artificial Neural Networks, and Deep Learning:
- Semi supervised learning, Reinforcement learning:
- Statistical Learning and Feature Engineering:
Sample Practice Questions
- Which of the following machine learning algorithms is an UNSUPERVISED learning algorithm used for clustering data into k distinct groups?
- In the Bias-Variance Tradeoff, what typically happens when a model's complexity is excessively high (e.g., a deep decision tree with no depth limit)?
- Which mathematical activation function is used in Logistic Regression to map any real-valued input z into a probability range between 0 and 1?
- How does L1 Regularization (Lasso) differ from L2 Regularization (Ridge) in linear models?
- In a medical diagnostic ML model for detecting a rare life-threatening disease, which evaluation metric is MOST critical to maximize to minimize missed positive cases (False Negatives)?
- What happens during Gradient Descent optimization if the learning rate α is set too large?
- Which metric measures node impurity in a Decision Tree using the formula H(S) = -∑ p_i * log2(p_i)?
- Why is feature scaling (Standardization / Normalization) CRITICAL before applying distance-based algorithms like K-Nearest Neighbors (KNN)?