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Python Mastery

Name: Python Mastery
Availability: InStock

with LaunchFar AI

10 weeks76 lessons

Production Python for AI and ML engineering: 76 hands-on challenges across 22 modules, from the runtime and core data structures through comprehensions, generators, regular expressions and a staged capstone build, every one of them running in your browser. Built for developers and data practitioners moving into AI engineering. Personalized to your skill level so you only see the modules you actually need.

Syllabus

1. Runtime and first steps
- Meet the runtime6 min
- First signals to stdout4 min
- Comments and multi-line output7 min
2. Variables and types
- Naming the pipeline's knobs5 min
- Rebinding config mid-run6 min
- The core types in a trace record5 min
- type() checks before the cast6 min
3. Numbers and math
- Cost math and precedence6 min
- Batching tokens with // and %5 min
4. Strings and text
- Indexing into model IDs5 min
- Slicing prompt prefixes5 min
- Assembling log lines with f-strings6 min
- Clean, split, join the raw log8 min
5. Conditions and branching
- Threshold checks4 min
- Compound guard clauses5 min
- The three-way branch7 min
- Routing requests by score band7 min
6. Loops and iteration
- Walking the trace batch6 min
- Polling until the job settles4 min
- Early exit on the poison record5 min
- Numbered log lines with enumerate4 min
7. Functions and scope
- The first helper4 min
- Parameterising the trace helper5 min
- Returning results (and more than one)7 min
- Sensible defaults for model params7 min
- Where names live: scope without surprises8 min
8. Error handling
- Catching the flaky call4 min
- Catching only what you mean5 min
- Raising your own errors, cleaning up always8 min
- The parser that refuses to crash7 min
9. Input, output and files
- Reading operator input5 min
- Casting the raw string5 min
- Persisting the run log8 min
- Paths as objects7 min
10. Modules and imports
- import styles, aliases, namespaces6 min
- Split the helper into its own file8 min
11. Lists
- Building the routing service's allow-list4 min
- Picking a model out of the routing list4 min
- Adding a new model to the routing list with `.append()`5 min
- in-checks and len on the batch5 min
12. Dictionaries
- Looking up a per-tenant config value by key4 min
- Adding a new model entry to the per-tenant config4 min
- Iterating a per-model price dict for the cost report4 min
13. Tuples, sets and None
- Reading an immutable allow-list snapshot tuple4 min
- Deduplicating a model-name list with a set5 min
- Marking a not-yet-rated trace with `None`3 min
- Guarding the cost aggregator against not-yet-set scores4 min
14. Comprehensions
- Building a list of squared latencies in one expression4 min
- Filtering high-latency calls with a comprehension `if`4 min
- Squaring only the even-indexed traces in one pass4 min
- Inverting the model-price table7 min
15. Nested data
- Walking a list of trace dicts to extract names4 min
- Walking a per-tenant config's list of allowed models4 min
- Rollup aggregations5 min
16. Iterators and generators
- iter/next on the trace stream6 min
- Lazy pipelines in one line6 min
- yield-ing tokens one at a time7 min
- Chained generators as a streaming ETL8 min
17. collections and itertools
- Counter for token frequency tables6 min
- Grouping traces by model6 min
- The sliding context window6 min
- islice, chain, groupby on batches8 min
18. Sorting and searching
- Ranking retrieval hits by score7 min
- Binary search and top-k without sorting twice7 min
19. Regular expressions
- Finding the model name in the log line7 min
- Extracting structured fields from raw text7 min
- Redacting PII from prompts7 min
20. Dates and times
- Trace timestamps without tears6 min
- Latency windows with timedelta6 min
21. Serialisation and formats
- Parsing the tool-call payload6 min
- Config files on disk6 min
- The cost report as CSV7 min
- Model artifacts and why pickle bites7 min
22. Capstone: Trace Analytics Pipeline
- Ingest and validate the raw export14 min
- Transform and aggregate the stream15 min
- Persist and report the rollup15 min

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1. Runtime and first steps

2. Variables and types

3. Numbers and math

4. Strings and text

5. Conditions and branching

6. Loops and iteration

7. Functions and scope

8. Error handling

9. Input, output and files

10. Modules and imports

11. Lists

12. Dictionaries

13. Tuples, sets and None

14. Comprehensions

15. Nested data

16. Iterators and generators

17. collections and itertools

18. Sorting and searching

19. Regular expressions

20. Dates and times

21. Serialisation and formats

22. Capstone: Trace Analytics Pipeline

Start this course