A list of most important algorithms and machine learning methods needed
by every software engineer and data scientist
- Optimized Linear Search/Chunk search
- Maze
- BFS/DFS
- Genetic Algorithm
- Divide and Conquer
- Simulated Annealing
- Ant Colony Optimization
- Backtracking
- Dynamic Programming and Memoization
- Topological Sort
- Travel Salesman Problem Optimization
- Branch and Bound Method
- Greedy Algorithm
- Beehive algorithm
- Naive Base Algorithm
- Support Vector Machine
- Neural Net (deep learning)
- Tabu Search
- Linear Regression
- Shebechev's algorithm
- Newton, secant and bisection methods
- Trapezoidal method
- Monte Carlo Method
- Balance Trees
- Bat Optimization Algorithm
- Central limit Theorem
- Shortest path optimization
- Advance Graph Algorithms: coloring problem, cost calculation, pruning , minimum spanning tree
- Van Emde Boas Tree
- Fusion trees
- Kd trees
- Pattern matching
- Binomial Heap
- Skip Lists
- Hashing theory
- Queueing theory
- Parallel programming
- Pattern recognition algorithms
- Induction and deduction
- Randomization algorithm
- Simpsons rule integration technique
- Clustering Algorithm
- Scheduling Algorithm
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- kNN
- k-Means
- Random Forest
