Introduction to Algorithms and Data Structures: A solid foundation for the real world of machine learning and data analytics

0. What You Will Learn & How to Get Help\n	0.1. Benefits of learning about algorithms and data structures\n	0.2. Course Structure\n1. Introduction to Algorithms\n	1.1. Playing a Counting Game\n		1.1.1. What is an Algorithm?\n		1.1.2. Guess the Number Game\n		1.1.3. Algorithm Guidelines\n		1.1.4. Practice Exercise 1\n		1.1.5. Answers to Practice Exercise 1\n		1.1.6. Evaluating Linear Search\n		1.1.7. Evaluating Binary Search\n		1.1.8. Practice Exercise 2\n		1.1.9. Answers to Practice Exercise 2\n	1.2. Time Complexity\n		1.2.1. Efficiency of an Algorithm\n		1.2.2. The Big O\n		1.2.3. Constant and Logarithmic Time\n		1.2.4. Linear & Quadratic Time\n		1.2.5. Cubic Runtime\n		1.2.6. Quasilinear Runtime\n		1.2.7. Polynomial Runtimes\n		1.2.8. Exponential Runtimes\n		1.2.9. How to Determine the Complexity of an Algorithm\n		1.2.10. Practice Exercise 3\n		1.2.11. Answers to Practice Exercise 3\n	1.3. Algorithms in Code\n		1.3.1. Linear Search in Code\n		1.3.2. Binary Search in Code\n		1.3.3. Recursive Binary Search in Code\n		1.3.4. Practice Exercise 4\n		1.3.5. Answers to Practice Exercise 4\n	1.4. Recursion and Space Complexity\n		1.4.1. Recursive Functions\n		1.4.2. Space Complexity\n		1.4.3. A Recap of What You Learned\n		1.4.4. Practice Exercise 5\n		1.4.5. Answers to Practice Exercise 5\n	1.5. Download Training Resources & Get Further Help