Programming Tutorials and Practice Problems

• Input/Output
  • Basics of Input/Output
• Complexity Analysis
  • Time and Space Complexity
• Implementation
  • Basics of Implementation
• Operators
  • Basics of Operators
• Bit Manipulation
  • Basics of Bit Manipulation
• Recursion
  • Recursion and Backtracking

• Arrays
  • 1-D
  • Multi-dimensional
• Stacks
  • Basics of Stacks
• Queues
  • Basics of Queues
• Hash Tables
  • Basics of Hash Tables
• Linked List
  • Singly Linked List
• Trees
  • Binary/ N-ary Trees
  • Binary Search Tree
  • Heaps/Priority Queues
• Advanced Data Structures
  • Trie (Keyword Tree)
  • Segment Trees
  • Fenwick (Binary Indexed) Trees
  • Suffix Trees
  • Suffix Arrays
• Disjoint Data Structures
  • Basics of Disjoint Data Structures

• Searching
  • Linear Search
  • Binary Search
  • Ternary Search
• Sorting
  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Merge Sort
  • Quick Sort
  • Counting Sort
  • Radix Sort
  • Heap Sort
  • Bucket Sort
• Greedy Algorithms
  • Basics of Greedy Algorithms
• Graphs
  • Graph Representation
  • Breadth First Search
  • Depth First Search
  • Minimum Spanning Tree
  • Shortest Path Algorithms
  • Flood-fill Algorithm
  • Articulation Points and Bridges
  • Biconnected Components
  • Strongly Connected Components
  • Topological Sort
  • Hamiltonian Path
  • Maximum flow
  • Minimum Cost Maximum Flow
  • Min-cut
• String Algorithms
  • Basics of String Manipulation
  • String Searching
  • Z Algorithm
  • Manachar’s Algorithm
• Dynamic Programming
  • Introduction to Dynamic Programming 1
  • 2 Dimensional
  • State space reduction
  • Dynamic Programming and Bit Masking

• Number Theory
  • Basic Number Theory-1
  • Basic Number Theory-2
  • Primality Tests
  • Totient Function
• Combinatorics
  • Basics of Combinatorics
  • Inclusion-Exclusion
• Geometry
  • Line Sweep Technique
  • Line Intersection using Bentley Ottmann Algorithm

• Statistics
  • Basic Probability Models and Rules
  • Bayes’ rules, Conditional probability, Chain rule
  • Discrete Random Variables
  • Continuous Random Variables
• Data Manipulation & Visualisation
  • Practical Tutorial on Data Manipulation with Numpy and Pandas in Python
• Machine Learning Algorithms
  • Beginners Guide to Regression Analysis and Plot Interpretations
  • Practical Guide to Logistic Regression Analysis in R
  • Practical Tutorial on Random Forest and Parameter Tuning in R
  • Practical Guide to Clustering Algorithms & Evaluation in R
  • Beginners Tutorial on XGBoost and Parameter Tuning in R
  • Deep Learning & Parameter Tuning with MXnet, H2o Package in R
  • Decision Tree
• Advanced Techniques
  • Simple Tutorial on Regular Expressions and String Manipulations in R
  • Practical Guide to Text Mining and Feature Engineering in R
  • Winning Tips on Machine Learning Competitions by Kazanova, Current Kaggle #3
• Machine Learning Projects
  • Practical Machine Learning Project in Python on House Prices Data
• Challenges Winning Approach
  • Challenge #1 - Machine Learning
  • Challenge #3 - Machine Learning
  • Challenge #2 - Deep Learning
• Transfer Learning
  • Transfer Learning Introduction