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
- A* Search
- Hill Climbing
- 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
- AO* Search
- Best First Search
-
Linear Algorithms. Tutorials on linear machine learning algorithms such as multivariate linear regression, logistic regression and the Perceptron algorithm.
Nonlinear Algorithms. Tutorials on nonlinear machine learning algorithms such as
Naive Bayes, k-Nearest Neighbors, Learning Vector Quantization, Backpropagation and
Decision Trees.
Ensemble Algorithms.
