A priority queue is used in Dijkstra's algorithm to efficiently retrieve the node with the smallest tentative distance.

A priority queue is used in Dijkstra's algorithm to efficiently select the node with the smallest tentative distance.

A priority queue is used in Dijkstra's algorithm to efficiently retrieve the vertex with the smallest distance from the source.

A priority queue is used in Dijkstra's algorithm to efficiently retrieve the vertex with the smallest distance estimate.

A priority queue is used to keep track of the shortest path estimates in Dijkstra's algorithm, allowing efficient retrieval of the next vertex with the smallest distance.

The 'tentative distance' represents the current best-known distance from the source node to the current node being processed.

The priority queue in Dijkstra's algorithm stores only the unvisited vertices, allowing the algorithm to efficiently select the next vertex with the smallest tentative distance.

The 'visited' set is used to keep track of the vertices that have already been processed to avoid processing the same vertex multiple times, ensuring efficiency.

Overlapping subproblems refer to subproblems that share sub-subproblems, allowing for reuse of results.

Overlapping subproblems refer to subproblems that are solved multiple times in the process of solving a larger problem.

In dynamic programming, a 'state' refers to a specific subproblem that is being solved.

Memoization is a technique used in dynamic programming to store the results of expensive function calls and reuse them when the same inputs occur again.

The optimal substructure property means that the optimal solution to a problem can be constructed from the optimal solutions of its subproblems.

Memoization helps avoid redundant calculations by storing the results of expensive function calls.

The bottom-up approach typically uses less memory than the top-down approach because it avoids the overhead of recursive calls.

The primary purpose of the table or array in dynamic programming is to store intermediate results to avoid redundant calculations.

A state transition equation describes how to move from one state to another in the dynamic programming solution, defining the relationship between subproblems.

Memoization is used to store intermediate results of subproblems to avoid redundant calculations and improve efficiency.

The base case is used to initialize the DP table and provide starting values for the recursive relations.

In dynamic programming, the 'state' represents the results of subproblems, which are used to build up the solution to the overall problem.

The table or array in dynamic programming is used to store intermediate results of subproblems, which helps in building up the solution to the overall problem.

The 'table' in dynamic programming is used to store intermediate results to avoid redundant calculations.

Optimal substructure refers to the property that a problem can be broken down into smaller, simpler subproblems.

Silhouette Score measures how similar an object is to its own cluster compared to other clusters.

One-hot encoding transforms categorical variables into a binary matrix, making them suitable for machine learning algorithms.

Normalization is the process of scaling features to a specific range, often [0, 1].

Neural networks are effective in fraud detection, analyzing transaction patterns to identify potentially fraudulent activities.

Agglomerative hierarchical clustering starts with each data point as its own cluster and merges them into larger clusters based on similarity.

Agglomerative clustering begins with each data point as its own cluster and merges them iteratively based on distance until a single cluster is formed.

Agglomerative clustering begins with each data point as its own cluster and progressively merges them based on their similarities.