タイムテーブル - Keith Galli - 機械学習のまとめ Keith Galliのタイムテーブルです。 https://ml.streamdb.net/timelines-rss/c/UCq6XkhO5SZ66N04IcPbqNcw Sat, 20 Apr 24 02:00:00 +0900 - Livestream Overview（00:00:00 - 00:04:00） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/0/e/240 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - About the Olympics dataset (source website and how it was scraped)（00:04:00 - 00:09:50） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/240/e/590 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Cleaning the dataset (getting started with code & data)（00:09:50 - 00:19:26） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/590/e/1166 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - What aspects of our data should be cleaned?（00:19:26 - 00:29:08） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/1166/e/1748 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Get rid of bullet points in Used name column（00:29:08 - 00:34:08） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/1748/e/2048 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - How to split Measurements into two separate height/weight numeric columns.（00:34:08 - 01:05:00） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/2048/e/3900 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Parse out dates from Born & Died columns（01:05:00 - 01:25:43） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/3900/e/5143 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Parse out city, region, and country from Born column (working with regular expressions)（01:25:43 - 01:41:15） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/5143/e/6075 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Get rid of the extra columns（01:41:15 - 01:46:08） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/6075/e/6368 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Next steps (how would we clean the results.csv)（01:46:08 - 01:49:41） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/6368/e/6581 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Questions & Answers（01:49:41 - 02:02:26） https://ml.streamdb.net/timelines/v/oad9tVEsfI0/s/6581/e/7346 Sat, 20 Apr 24 02:00:00 +0900 Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) - Intro & Setup（00:00:00 - 00:02:14） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/0/e/134 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) great video! however, regarding the usage of the terminal to create directories etc at , can anyone recommend some youtube videos or sources to get more familiar with it? thanks a bunch! good luck getting good at pandas everybody :)（00:00:59 - 05:20:18） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/59/e/19218 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Problems (1-3) Initial pandas setup（00:02:14 - 00:04:42） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/134/e/282 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Problems (4-10) DataFrame operations（00:04:42 - 00:04:52） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/282/e/292 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 4) Create a dataframe from dictionary（00:04:52 - 00:05:24） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/292/e/324 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 5) Display dataframe summary（00:05:24 - 00:05:41） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/324/e/341 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 6) First 3 rows of the dataframe（00:05:41 - 00:06:02） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/341/e/362 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 7) Select ‘animal’ and ‘age’ columns（00:06:02 - 00:07:42） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/362/e/462 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 8) Data in specific rows and columns（00:07:42 - 00:09:06） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/462/e/546 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 9) Rows with visits greater than 3（00:09:06 - 00:09:57） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/546/e/597 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 10) Rows with NaN in age（00:09:57 - 00:10:56） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/597/e/656 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 11) Cats younger than 3 years（00:10:56 - 00:11:35） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/656/e/695 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 12) Age between 2 and 4（00:11:35 - 00:12:45） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/695/e/765 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 13) Change age in row ‘f’（00:12:45 - 00:15:56） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/765/e/956 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 14) Sum of all visits（00:15:56 - 00:16:41） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/956/e/1001 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 15) Average age by animal（00:16:41 - 00:20:21） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1001/e/1221 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) I do this a lot, by passing a dict to the agg function after grouping (it allows you to asign multiple operators to several cols at once). Eg df.groupby(“animal”).agg({“age”:”mean”})（00:19:30 - 05:20:18） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1170/e/19218 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 16) Modify and revert rows（00:20:21 - 00:24:06） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1221/e/1446 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 17) Count by animal type（00:24:06 - 00:25:28） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1446/e/1528 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Quick review（00:25:28 - 00:26:17） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1528/e/1577 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 18) Sort by age and visits（00:26:17 - 00:28:07） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1577/e/1687 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 19) Convert 'priority' to boolean（00:28:07 - 00:29:42） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1687/e/1782 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 20) Replace 'snake' with 'python'（00:29:42 - 00:30:53） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1782/e/1853 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 21) Mean age by animal and visits（00:30:53 - 00:33:49） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/1853/e/2029 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Advanced DataFrame techniques（00:33:49 - 00:33:57） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/2029/e/2037 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 22) Filter duplicate integers（00:33:57 - 00:43:18） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/2037/e/2598 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 23) Subtract row mean（00:43:18 - 00:45:42） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/2598/e/2742 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 24) Column with smallest sum（00:45:42 - 00:50:39） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/2742/e/3039 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 25) Count unique rows（00:50:39 - 00:53:17） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/3039/e/3197 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 26) Column with third NaN（00:53:17 - 01:10:27） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/3197/e/4227 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Solution review for 26（01:10:27 - 01:17:13） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/4227/e/4633 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 27) Sum of top three values（01:17:13 - 01:24:01） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/4633/e/5041 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 28) Sum by column condition（01:24:01 - 01:40:11） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/5041/e/6011 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Recent problem review（01:40:11 - 01:42:53） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/6011/e/6173 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 29) Count differences since last zero（01:42:53 - 01:56:19） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/6173/e/6979 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 30) Locate largest values（01:56:19 - 02:08:38） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/6979/e/7718 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 31) Replace negatives with mean（02:08:38 - 02:17:43） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/7718/e/8263 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 32) Rolling mean over groups（02:17:43 - 02:23:10） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/8263/e/8590 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Series and DatetimeIndex（02:23:10 - 02:23:12） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/8590/e/8592 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 33) DatetimeIndex for 2015（02:23:12 - 02:27:56） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/8592/e/8876 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 34) Sum values on Wednesdays（02:27:56 - 02:45:04） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/8876/e/9904 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 35) Monthly mean values（02:45:04 - 02:46:16） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/9904/e/9976 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 36) Best value in four-month groups（02:46:16 - 02:50:26） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/9976/e/10226 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 37) DatetimeIndex of third Thursdays（02:50:26 - 02:59:03） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/10226/e/10743 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Cleaning Data（02:59:03 - 02:59:40） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/10743/e/10780 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 38) Fill missing FlightNumber（02:59:40 - 03:02:45） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/10780/e/10965 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 39) Split column by delimiter（03:02:45 - 03:06:47） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/10965/e/11207 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 40) Fix city name capitalization（03:06:47 - 03:08:30） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/11207/e/11310 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 41) Reattach columns（03:08:30 - 03:13:11） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/11310/e/11591 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 42) Fix airline name punctuation（03:13:11 - 03:17:45） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/11591/e/11865 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 43) Expand RecentDelays into columns（03:17:45 - 03:27:31） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/11865/e/12451 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - MultiIndexes in Pandas（03:27:31 - 03:27:34） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12451/e/12454 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 44) Construct a MultiIndex（03:27:34 - 03:30:37） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12454/e/12637 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Solution review（03:30:37 - 03:32:44） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12637/e/12764 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 45) Lexicographically sorted check（03:32:44 - 03:32:58） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12764/e/12778 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 46) Select specific MultiIndex labels（03:32:58 - 03:34:23） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12778/e/12863 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 47) Slice Series with MultiIndex（03:34:23 - 03:35:24） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12863/e/12924 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 48) Sum by first level（03:35:24 - 03:37:47） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/12924/e/13067 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 49) Alternative sum method（03:37:47 - 03:40:08） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/13067/e/13208 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Additional solution insights（03:40:08 - 03:41:22） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/13208/e/13282 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 50) Swap MultiIndex levels（03:41:22 - 03:45:27） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/13282/e/13527 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Minesweeper problems（03:45:27 - 03:45:44） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/13527/e/13544 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 51) Generate coordinate grid（03:45:44 - 04:00:28） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/13544/e/14428 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 52) Add 'safe' or 'mine' column（04:00:28 - 04:03:04） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/14428/e/14584 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 53) Count adjacent mines（04:03:04 - 04:27:33） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/14584/e/16053 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Review solution to 53（04:27:33 - 04:33:02） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/16053/e/16382 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Skipped problems 54 & 55（04:33:02 - 04:33:11） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/16382/e/16391 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Plotting（04:33:11 - 04:33:12） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/16391/e/16392 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 56) Scatter plot with black x markers（04:33:12 - 04:41:26） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/16392/e/16886 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 57) Plot four data types（04:41:26 - 04:52:50） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/16886/e/17570 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 58) Overlay multiple graphs（04:52:50 - 05:03:11） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/17570/e/18191 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 59) Hourly stock data summary（05:03:11 - 05:14:12） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/18191/e/18852 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - 60) Candlestick plot（05:14:12 - 05:20:18） https://ml.streamdb.net/timelines/v/i7v2m-ebXB4/s/18852/e/19218 Sun, 14 Apr 24 02:43:22 +0900 Solving 100 Python Pandas Problems! (from easy to very difficult) - Intro & Live Stream Overview（00:00:00 - 00:04:58） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/0/e/298 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How over saturated is the data science job market and will things improve in your opinion?（00:04:58 - 00:07:26） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/298/e/446 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How much maths is needed to get a data science job?（00:07:26 - 00:09:55） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/446/e/595 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Can you share a basic roadmap to learn generative AI and LLMs?（00:09:55 - 00:13:08） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/595/e/788 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - What future-proof tech career to should someone focus on who’s looking to change career?（00:13:08 - 00:16:52） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/788/e/1012 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Is data structures & algorithms (dsa) necessary to get a job in data science?（00:16:52 - 00:19:17） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1012/e/1157 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How to get good at data structures and algorithms?（00:19:17 - 00:22:20） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1157/e/1340 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Why don’t you make videos regularly now?（00:22:20 - 00:24:18） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1340/e/1458 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How much do you need to know for entry-level roles / college internships?（00:24:18 - 00:27:02） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1458/e/1622 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How important is domain knowledge for data science?（00:27:02 - 00:29:29） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1622/e/1769 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts（00:29:29 - 00:32:30） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1769/e/1950 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Any good data projects to increase visibility to companies?（00:32:30 - 00:36:05） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/1950/e/2165 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Do you think we should all learn vector databases?（00:36:05 - 00:39:10） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/2165/e/2350 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Is webscraping illegal? what can I do and not do?（00:39:10 - 00:43:14） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/2350/e/2594 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - What are you working on at the moment?（00:43:14 - 00:45:25） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/2594/e/2725 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How can I turn a financial database I’m building into an interesting portfolio project to showcase work?（00:45:25 - 00:49:23） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/2725/e/2963 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - What advice do you have for data scientists who want to get into freelance/consulting?（00:49:23 - 00:55:15） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/2963/e/3315 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - What are important skills for DS beyond ML & AI?（00:55:15 - 00:59:42） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/3315/e/3582 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Do I need to become a full-stack programmer to have success in this field?（00:59:42 - 01:02:31） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/3582/e/3751 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - If you weren’t allowed to do programming or create content, what would you do?（01:02:31 - 01:03:39） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/3751/e/3819 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - How did you achieve your advanced height? Asking for a friend.（01:03:39 - 01:04:23） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/3819/e/3863 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Final thoughts. Thanks for coming!-------------------------Follow me on social media!Instagram | https://www.instagram.com/keithgalli/Twitter | https://twitter.com/keithgalliTikTok | https://tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!https://stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - https://www.patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.（01:04:23 - 01:05:26） https://ml.streamdb.net/timelines/v/YXANJ20--vA/s/3863/e/3926 Sun, 07 Apr 24 01:00:00 +0900 Ask me anything! (data science, LLMs, landing a job, and more) - Video Overview & Reference Material（00:00:00 - 00:03:05） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/0/e/185 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Data & Code Setup（00:03:05 - 00:05:04） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/185/e/304 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #0: Configure LLM to use with Python (OpenAI API)（00:05:04 - 00:20:10） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/304/e/1210 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama)（00:20:10 - 00:27:39） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/1210/e/1659 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #1: Use LLM to Parse Simple Sentence Examples（00:27:39 - 00:41:22） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/1659/e/2482 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Sub-task #1: Convert string to Python Object（00:41:22 - 00:44:29） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/2482/e/2669 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain（00:44:29 - 00:56:24） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/2669/e/3384 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Quick note on a benefit of using LangChain (easily switching between models)（00:56:24 - 00:58:06） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/3384/e/3486 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe（00:58:06 - 01:06:22） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/3486/e/3982 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #2: Connect Pages that Belong to the Same Documents（01:06:22 - 01:56:36） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/3982/e/6996 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) Fantastic real world problem as a lot of your other videos. I've got to say that all models on Ollama absolutely stink in comparison to OpenAI. However I have been using a preprocessing text function I created for using in a news article project I'm working on using Spacy. I have been able to pass the transcription_text's through my function with some minor tweaking and have been able to recreate what the LLM's are doing just through code, by using the doc.ents functionality. Only through the video at the moment and perhaps you use something similar later on, but Spacy has been a bit of a godsend if you don't/can't pay for OpenAI（01:27:00 - 02:39:33） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/5220/e/9573 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #3: Parse out values from merged documents（01:56:36 - 02:12:44） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/6996/e/7964 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #4 (setup): Analyze Results（02:12:44 - 02:17:52） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/7964/e/8272 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Fixing up our results from task #3 quickly（02:17:52 - 02:20:41） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/8272/e/8441 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Task #4: Find the average age of apprentices in our merged contract documents（02:20:41 - 02:30:59） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/8441/e/9059 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Other analysis, wlho had the most apprentices?（02:30:59 - 02:39:33） https://ml.streamdb.net/timelines/v/MeyVptCRubI/s/9059/e/9573 Thu, 21 Mar 24 01:02:42 +0900 Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) - Introduction（00:00:00 - 00:01:50） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/0/e/110 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Tip 1: Show Private Repository Activity（00:01:50 - 00:02:57） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/110/e/177 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Tip 2: Highlight best work using pins（00:02:57 - 00:04:13） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/177/e/253 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Tip 3: Create a Profile README（00:04:13 - 00:11:32） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/253/e/692 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Tip 4: Fill in all Profile Details（00:11:32 - 00:13:45） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/692/e/825 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Tip 5: Fill in READMEs on highlighted repos（00:13:45 - 00:19:01） https://ml.streamdb.net/timelines/v/QDfLou004iE/s/825/e/1141 Tue, 27 Feb 24 01:39:57 +0900 How to make your GitHub more impressive to Employers! (5 simple tips) - Overview & Getting Started（00:00:00 - 00:00:50） https://ml.streamdb.net/timelines/v/bY3JTdF0Wmk/s/0/e/50 Wed, 27 Dec 23 13:31:07 +0900 Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas) - 1. Predicting Heart Attack Risk (Easy Problem)（00:00:50 - 00:06:44） https://ml.streamdb.net/timelines/v/bY3JTdF0Wmk/s/50/e/404 Wed, 27 Dec 23 13:31:07 +0900 Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas) - 2. Data Anonymization (Medium Problem)（00:06:44 - 00:11:53） https://ml.streamdb.net/timelines/v/bY3JTdF0Wmk/s/404/e/713 Wed, 27 Dec 23 13:31:07 +0900 Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas) - 3. Dessert Duel (Hard Problem)（00:11:53 - 00:29:59） https://ml.streamdb.net/timelines/v/bY3JTdF0Wmk/s/713/e/1799 Wed, 27 Dec 23 13:31:07 +0900 Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas) - Overview（00:00:00 - 00:00:41） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/0/e/41 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Getting started on the Book Review API（00:00:41 - 00:02:20） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/41/e/140 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Set up Airtable as our database & connect to it with Python（00:02:20 - 00:10:44） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/140/e/644 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Implement code to add reviews and view all reviews（00:10:44 - 00:31:40） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/644/e/1900 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Adding a POST request to our API（00:31:40 - 00:36:40） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/1900/e/2200 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Trying out our new endpoints (using documentation & requests library of Python)（00:36:40 - 00:41:32） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/2200/e/2492 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Commit code to Github & deploy live to Render.com（00:41:32 - 00:50:46） https://ml.streamdb.net/timelines/v/rCrDYRBOuNw/s/2492/e/3046 Mon, 04 Dec 23 01:01:35 +0900 Python Project: Implement a REST API with Flask & Flasgger Libraries! - Video overview（00:00:00 - 00:01:18） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/0/e/78 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - What we're building（00:01:18 - 00:03:20） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/78/e/200 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - How to get setup with Github template code（00:03:20 - 00:07:00） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/200/e/420 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Taking a look at the Flask, Flasgger Python3 code（00:07:00 - 00:08:38） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/420/e/518 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Testing some API requests (GET) locally（00:08:38 - 00:13:09） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/518/e/789 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Building another GET request endpoint (with multiple parameters)（00:13:09 - 00:14:34） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/789/e/874 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Using ChatGPT to help us build another endpoint（00:14:34 - 00:22:43） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/874/e/1363 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Deploying our API to a live public URL endpoint (using render.com)（00:22:43 - 00:29:27） https://ml.streamdb.net/timelines/v/moi8WPO3Xhs/s/1363/e/1767 Sun, 26 Nov 23 22:57:11 +0900 How to create & deploy an API in Python! (with interactive documentation) - Video overview & topics covered（00:00:00 - 00:01:43） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/0/e/103 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Basic regex syntax (building up an intuition)（00:01:43 - 00:04:23） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/103/e/263 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Character Sets Overview ([A-Za-z0-9])（00:04:23 - 00:05:57） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/263/e/357 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Quantifiers Guide (*, +, ?, {3,5})（00:05:57 - 00:09:30） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/357/e/570 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Guided Exercise: Find all words that don't use vowels（00:09:30 - 00:11:08） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/570/e/668 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) Linguistically speaking, [y] can be a vowel, especially in words like "crypt". Pedantry of course, since it could just be added into the regex if needed. 🤓（00:10:50 - 00:36:40） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/650/e/2200 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Helpful cheat sheet to remember regex syntax in the real-world（00:11:08 - 00:12:47） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/668/e/767 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Matching words/patterns of a specific length ({3,5})（00:12:47 - 00:14:58） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/767/e/898 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - OR operator overview（00:14:58 - 00:17:14） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/898/e/1034 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Guided Exercise: Match valid sentences (starts with capital letter, ends with period)（00:17:14 - 00:21:18） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/1034/e/1278 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Character classes overview (\w, \b, \d, \s)（00:21:18 - 00:23:13） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/1278/e/1393 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Escaping Characters（00:23:13 - 00:25:02） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/1393/e/1502 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Practice Exercise #1: Write a regular expression to match meme text format（00:25:02 - 00:30:39） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/1502/e/1839 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Practice Exercise #2: Write a regular expression to match a specific date format（00:30:39 - 00:39:03） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/1839/e/2343 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) Might not really be up to regex to do data validation. There are better tools for that. 🧰In fact, integrating these into data workflows would be a good follow-up video for the future. ▶（00:36:40 - 01:19:21） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/2200/e/4761 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Groups overview（00:39:03 - 00:50:16） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/2343/e/3016 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) You could definitely get everything if you add an extra parenthesis around the thing you want to get in this case (([a-z][A-Z])+[a-z]?)@(\w+\.\w+)（00:49:02 - 01:19:21） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/2942/e/4761 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Lookahead & Lookbehind Assertions（00:50:16 - 01:00:18） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/3016/e/3618 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Practice Exercise #3: Detect if same word pops up multiple times in a sentence（01:00:18 - 01:06:04） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/3618/e/3964 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Practice Exercise #4: Password matching with rules（01:06:04 - 01:16:16） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/3964/e/4576 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice) - Some final recommendations! (additional practice, chatgpt, etc.)（01:16:16 - 01:19:21） https://ml.streamdb.net/timelines/v/vsa9GGzMFXQ/s/4576/e/4761 Thu, 13 Apr 23 23:39:51 +0900 Complete Regular Expressions Tutorial! (with exercises for practice)