My project work from the edX offering of MIT's 6.00.1x and 6.00.2x courses in Python
6.00.1x -> MIT's introductory computer science course, taught in Python and covering:
- Basic programming constructs (conditional statements, control flow, loops, recursion)
- Basic Python data structures (lists, tuples, dicts)
- Problem solving with string indexing, hashing, sorting algorithms
6.00.2x -> MIT's intermediate CS course focusing on computational solving skills:
- Dynamic programming
- Knapsack problem and greedy optimization
- K-means clustering
- Graph optimization (depth first, breadth first tree searching, shortest path, etc.)
- Statistics for ML (sampling, linear regression, cross validation, inference testing and confidence intervals)