- Analysis & Evaluation (using clf.score() method)（00:53:35 - 00:56:58） - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Analysis & Evaluation (using clf.score() method)（00:53:35 - 00:56:58）
Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith

In this video we walk through a real world python machine learning project using the sci-kit learn library. In it we work our way to building a model that automatically classifies...
Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith

In this video we walk through a real world python machine learning project using the sci-kit learn library. In it we work our way to building a model that automatically classifies text as either having a positive or negative sentiment. We do this by using amazon reviews as our training data. Full video timeline in the comments!

Link to Code & Data:
https://github.com/keithgalli/sklearn

Raw Data download:
http://jmcauley.ucsd.edu/data/amazon/

Sci-kit learn documentation:
https://scikit-learn.org/stable/documentation.html

Make sure you have sci-kit learn downloaded! To do this either run "pip install sklearn" or use python through Anaconda.

Join the Python Army to get access to perks!
YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join
Patreon - https://www.patreon.com/keithgalli

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Instagram: https://www.instagram.com/keithgalli/
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https://www.instagram.com/pagandvls/

---------------------------

Video outline!
0:00 - What we will be doing!
3:40 - Sci-Kit Learn Overview
6:38 - How do we find training data?
9:33 - Download data
11:45 - Load our data into Jupyter Notebook
16:38 - Cleaning our code a bit (building data class)
20:13 - Using Enums
22:50 - Converting text to numerical vectors, bag of words (BOW) explanation
25:45 - Training/Test Split (make sure to "pip install sklearn" !)
33:45 - Bag of words in sklearn (CountVectorizer)
40:05 - fit_transform, fit, transform methods
42:05 - Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification
47:50 - predict method
53:35 - Analysis & Evaluation (using clf.score() method)
56:58 - F1 score
1:01:01 - Improving our model (evenly distributing positive & negative examples and loading in more data)
1:20:36 - Let's see our model in action! (qualitative testing)
1:22:24 - Tfidf Vectorizer
1:25:40 - GridSearchCv to automatically find the best parameters
1:31:30 - Further NLP improvement opportunities
1:32:50 - Saving our model (Pickle) and reloading it later
1:36:37 - Category Classifier
1:39:14 - Confusion Matrix

---------------------
If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs

*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.

#Keith Galli #MIT #sklearn #python machine learning #nlp #machine learning project #artificial intelligence #sci kit learn #sci-kit learn #AI #python 3 #jupyter notebook #data science #ML #python data science #model selection #classification #regression #algorithms #sklearn overview #machine learning in python #python programming #programming #advanced #simple #complete #save model #confusion matrix #python plotting #sentiment #natural language processing #project #machine learning

スペースでAND検索・ORで区切るとOR検索・先頭に「-」で除外検索

- What we will be doing! - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- What we will be doing!

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:00:20 - 00:03:40
i wonder what the outcome will be for sarcasm, something like: 'beautiful restaurant that made me puke, raccomand' - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

i wonder what the outcome will be for sarcasm, something like: 'beautiful restaurant that made me puke, raccomand'

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:01:05 - 01:40:49
- Sci-Kit Learn Overview - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Sci-Kit Learn Overview

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:03:40 - 00:06:38
- How do we find training data? - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- How do we find training data?

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:06:38 - 00:09:33
- Download data - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Download data

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:09:33 - 00:11:45
- Load our data into Jupyter Notebook - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Load our data into Jupyter Notebook

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:11:45 - 00:16:38
- Cleaning our code a bit (building data class) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Cleaning our code a bit (building data class)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:16:38 - 00:20:13
- Using Enums - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Using Enums

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:20:13 - 00:22:50
- Converting text to numerical vectors, bag of words (BOW) explanation - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Converting text to numerical vectors, bag of words (BOW) explanation

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:22:50 - 00:25:45
- Training/Test Split (make sure to "pip install sklearn" !) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Training/Test Split (make sure to "pip install sklearn" !)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:25:45 - 00:33:45
Hey Keith, I am facing an issue reproducing your code. On your video at , there is an object named training that you print as training[0]. It doesn’t work on my side, but I’m unable to understand the reason. It looks like the training[0] is not accessible, doesn’t exist, don’t know exactly what’s happening.. Do you know if something changed on train_test_split function or pandas dataframe since you published your tutorial? Thanks in advance. - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

Hey Keith, I am facing an issue reproducing your code. On your video at , there is an object named training that you print as training[0]. It doesn’t work on my side, but I’m unable to understand the reason. It looks like the training[0] is not accessible, doesn’t exist, don’t know exactly what’s happening.. Do you know if something changed on train_test_split function or pandas dataframe since you published your tutorial? Thanks in advance.

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:31:15 - 01:40:49
- Bag of words in sklearn (CountVectorizer) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Bag of words in sklearn (CountVectorizer)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:33:45 - 00:40:05
Bro, I didnt get why you used Train_X as vectorizer? at ? What is the actual reason as we have a huge dataset or 670 rows n hundreds of column :/ - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

Bro, I didnt get why you used Train_X as vectorizer? at ? What is the actual reason as we have a huge dataset or 670 rows n hundreds of column :/

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:38:53 - 01:40:49
- fit_transform, fit, transform methods - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- fit_transform, fit, transform methods

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:40:05 - 00:42:05
- Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:42:05 - 00:47:50
He's referring to Patrick Winston. By sheer chance I was watching one of his lectures on YT early this morning. - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

He's referring to Patrick Winston. By sheer chance I was watching one of his lectures on YT early this morning.

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:43:00 - 01:40:49
I am at the mark. - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

I am at the mark.

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:44:00 - 01:40:49
- predict method - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- predict method

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:47:50 - 00:53:35
- Analysis & Evaluation (using clf.score() method) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Analysis & Evaluation (using clf.score() method)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:53:35 - 00:56:58
- F1 score - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- F1 score

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
00:56:58 - 01:01:01
- Improving our model (evenly distributing positive & negative examples and loading in more data) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Improving our model (evenly distributing positive & negative examples and loading in more data)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:01:01 - 01:20:36
- Let's see our model in action! (qualitative testing) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Let's see our model in action! (qualitative testing)

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:20:36 - 01:22:24
- Tfidf Vectorizer - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Tfidf Vectorizer

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:22:24 - 01:25:40
- GridSearchCv to automatically find the best parameters - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- GridSearchCv to automatically find the best parameters

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:25:40 - 01:31:30
- Further NLP improvement opportunities - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Further NLP improvement opportunities

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:31:30 - 01:32:50
- Saving our model (Pickle) and reloading it later - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Saving our model (Pickle) and reloading it later

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:32:50 - 01:36:37
- Category Classifier - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Category Classifier

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:36:37 - 01:39:14
- Confusion Matrix - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Confusion Matrix

Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)
2019年10月01日　
01:39:14 - 01:40:49
チャンネル登録YouTube RSS

Keith Galli

※本サイトに掲載されているチャンネル情報や動画情報はYouTube公式のAPIを使って取得・表示しています。

概要 カレンダー 動画一覧 タイムテーブル チャンネル分析

Timetable

動画タイムテーブル

スペースでAND検索・ORで区切るとOR検索・先頭に「-」で除外検索

動画数：87件

- Intro & Setup - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Intro & Setup

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:00:00 - 00:02:14
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 :) - 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 :)

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　 @realzeejay 様　
00:00:59 - 05:20:18
- Problems (1-3) Initial pandas setup - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Problems (1-3) Initial pandas setup

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:02:14 - 00:04:42
- Problems (4-10) DataFrame operations - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Problems (4-10) DataFrame operations

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:04:42 - 00:04:52
- 4) Create a dataframe from dictionary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 4) Create a dataframe from dictionary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:04:52 - 00:05:24
- 5) Display dataframe summary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 5) Display dataframe summary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:05:24 - 00:05:41
- 6) First 3 rows of the dataframe - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 6) First 3 rows of the dataframe

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:05:41 - 00:06:02
- 7) Select ‘animal’ and ‘age’ columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 7) Select ‘animal’ and ‘age’ columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:06:02 - 00:07:42
- 8) Data in specific rows and columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 8) Data in specific rows and columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:07:42 - 00:09:06
- 9) Rows with visits greater than 3 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 9) Rows with visits greater than 3

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:09:06 - 00:09:57
- 10) Rows with NaN in age - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 10) Rows with NaN in age

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:09:57 - 00:10:56
- 11) Cats younger than 3 years - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 11) Cats younger than 3 years

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:10:56 - 00:11:35
- 12) Age between 2 and 4 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 12) Age between 2 and 4

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:11:35 - 00:12:45
- 13) Change age in row ‘f’ - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 13) Change age in row ‘f’

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:12:45 - 00:15:56
- 14) Sum of all visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 14) Sum of all visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:15:56 - 00:16:41
- 15) Average age by animal - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 15) Average age by animal

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:16:41 - 00:20:21
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”}) - 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”})

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　 @AgustinGonzalez-tz3yr 様　
00:19:30 - 05:20:18
- 16) Modify and revert rows - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 16) Modify and revert rows

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:20:21 - 00:24:06
- 17) Count by animal type - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 17) Count by animal type

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:24:06 - 00:25:28
- Quick review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Quick review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:25:28 - 00:26:17
- 18) Sort by age and visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 18) Sort by age and visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:26:17 - 00:28:07
- 19) Convert 'priority' to boolean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 19) Convert 'priority' to boolean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:28:07 - 00:29:42
- 20) Replace 'snake' with 'python' - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 20) Replace 'snake' with 'python'

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:29:42 - 00:30:53
- 21) Mean age by animal and visits - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 21) Mean age by animal and visits

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:30:53 - 00:33:49
- Advanced DataFrame techniques - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Advanced DataFrame techniques

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:33:49 - 00:33:57
- 22) Filter duplicate integers - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 22) Filter duplicate integers

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:33:57 - 00:43:18
- 23) Subtract row mean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 23) Subtract row mean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:43:18 - 00:45:42
- 24) Column with smallest sum - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 24) Column with smallest sum

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:45:42 - 00:50:39
- 25) Count unique rows - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 25) Count unique rows

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:50:39 - 00:53:17
- 26) Column with third NaN - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 26) Column with third NaN

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
00:53:17 - 01:10:27
- Solution review for 26 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Solution review for 26

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:10:27 - 01:17:13
- 27) Sum of top three values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 27) Sum of top three values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:17:13 - 01:24:01
- 28) Sum by column condition - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 28) Sum by column condition

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:24:01 - 01:40:11
- Recent problem review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Recent problem review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:40:11 - 01:42:53
- 29) Count differences since last zero - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 29) Count differences since last zero

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:42:53 - 01:56:19
- 30) Locate largest values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 30) Locate largest values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
01:56:19 - 02:08:38
- 31) Replace negatives with mean - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 31) Replace negatives with mean

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:08:38 - 02:17:43
- 32) Rolling mean over groups - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 32) Rolling mean over groups

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:17:43 - 02:23:10
- Series and DatetimeIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Series and DatetimeIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:23:10 - 02:23:12
- 33) DatetimeIndex for 2015 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 33) DatetimeIndex for 2015

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:23:12 - 02:27:56
- 34) Sum values on Wednesdays - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 34) Sum values on Wednesdays

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:27:56 - 02:45:04
- 35) Monthly mean values - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 35) Monthly mean values

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:45:04 - 02:46:16
- 36) Best value in four-month groups - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 36) Best value in four-month groups

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:46:16 - 02:50:26
- 37) DatetimeIndex of third Thursdays - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 37) DatetimeIndex of third Thursdays

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:50:26 - 02:59:03
- Cleaning Data - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Cleaning Data

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:59:03 - 02:59:40
- 38) Fill missing FlightNumber - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 38) Fill missing FlightNumber

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
02:59:40 - 03:02:45
- 39) Split column by delimiter - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 39) Split column by delimiter

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:02:45 - 03:06:47
- 40) Fix city name capitalization - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 40) Fix city name capitalization

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:06:47 - 03:08:30
- 41) Reattach columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 41) Reattach columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:08:30 - 03:13:11
- 42) Fix airline name punctuation - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 42) Fix airline name punctuation

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:13:11 - 03:17:45
- 43) Expand RecentDelays into columns - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 43) Expand RecentDelays into columns

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:17:45 - 03:27:31
- MultiIndexes in Pandas - Solving 100 Python Pandas Problems! (from easy to very difficult)

- MultiIndexes in Pandas

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:27:31 - 03:27:34
- 44) Construct a MultiIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 44) Construct a MultiIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:27:34 - 03:30:37
- Solution review - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Solution review

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:30:37 - 03:32:44
- 45) Lexicographically sorted check - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 45) Lexicographically sorted check

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:32:44 - 03:32:58
- 46) Select specific MultiIndex labels - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 46) Select specific MultiIndex labels

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:32:58 - 03:34:23
- 47) Slice Series with MultiIndex - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 47) Slice Series with MultiIndex

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:34:23 - 03:35:24
- 48) Sum by first level - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 48) Sum by first level

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:35:24 - 03:37:47
- 49) Alternative sum method - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 49) Alternative sum method

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:37:47 - 03:40:08
- Additional solution insights - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Additional solution insights

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:40:08 - 03:41:22
- 50) Swap MultiIndex levels - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 50) Swap MultiIndex levels

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:41:22 - 03:45:27
- Minesweeper problems - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Minesweeper problems

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:45:27 - 03:45:44
- 51) Generate coordinate grid - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 51) Generate coordinate grid

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
03:45:44 - 04:00:28
- 52) Add 'safe' or 'mine' column - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 52) Add 'safe' or 'mine' column

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:00:28 - 04:03:04
- 53) Count adjacent mines - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 53) Count adjacent mines

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:03:04 - 04:27:33
- Review solution to 53 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Review solution to 53

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:27:33 - 04:33:02
- Skipped problems 54 & 55 - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Skipped problems 54 & 55

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:33:02 - 04:33:11
- Plotting - Solving 100 Python Pandas Problems! (from easy to very difficult)

- Plotting

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:33:11 - 04:33:12
- 56) Scatter plot with black x markers - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 56) Scatter plot with black x markers

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:33:12 - 04:41:26
- 57) Plot four data types - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 57) Plot four data types

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:41:26 - 04:52:50
- 58) Overlay multiple graphs - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 58) Overlay multiple graphs

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
04:52:50 - 05:03:11
- 59) Hourly stock data summary - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 59) Hourly stock data summary

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
05:03:11 - 05:14:12
- 60) Candlestick plot - Solving 100 Python Pandas Problems! (from easy to very difficult)

- 60) Candlestick plot

Solving 100 Python Pandas Problems! (from easy to very difficult)
2024年04月14日　
05:14:12 - 05:20:18
- Intro & Live Stream Overview - Ask me anything! (data science, LLMs, landing a job, and more)

- Intro & Live Stream Overview

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:00:00 - 00:04:58
- How over saturated is the data science job market and will things improve in your opinion? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:04:58 - 00:07:26
- How much maths is needed to get a data science job? - Ask me anything! (data science, LLMs, landing a job, and more)

- How much maths is needed to get a data science job?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:07:26 - 00:09:55
- Can you share a basic roadmap to learn generative AI and LLMs? - Ask me anything! (data science, LLMs, landing a job, and more)

- Can you share a basic roadmap to learn generative AI and LLMs?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:09:55 - 00:13:08
- What future-proof tech career to should someone focus on who’s looking to change career? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:13:08 - 00:16:52
- Is data structures & algorithms (dsa) necessary to get a job in data science? - Ask me anything! (data science, LLMs, landing a job, and more)

- Is data structures & algorithms (dsa) necessary to get a job in data science?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:16:52 - 00:19:17
- How to get good at data structures and algorithms? - Ask me anything! (data science, LLMs, landing a job, and more)

- How to get good at data structures and algorithms?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:19:17 - 00:22:20
- Why don’t you make videos regularly now? - Ask me anything! (data science, LLMs, landing a job, and more)

- Why don’t you make videos regularly now?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:22:20 - 00:24:18
- How much do you need to know for entry-level roles / college internships? - Ask me anything! (data science, LLMs, landing a job, and more)

- How much do you need to know for entry-level roles / college internships?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:24:18 - 00:27:02
- How important is domain knowledge for data science? - Ask me anything! (data science, LLMs, landing a job, and more)

- How important is domain knowledge for data science?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:27:02 - 00:29:29
- Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts - Ask me anything! (data science, LLMs, landing a job, and more)

- Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:29:29 - 00:32:30
- Any good data projects to increase visibility to companies? - Ask me anything! (data science, LLMs, landing a job, and more)

- Any good data projects to increase visibility to companies?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:32:30 - 00:36:05
- Do you think we should all learn vector databases? - Ask me anything! (data science, LLMs, landing a job, and more)

- Do you think we should all learn vector databases?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:36:05 - 00:39:10
- Is webscraping illegal? what can I do and not do? - Ask me anything! (data science, LLMs, landing a job, and more)

- Is webscraping illegal? what can I do and not do?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:39:10 - 00:43:14
- What are you working on at the moment? - Ask me anything! (data science, LLMs, landing a job, and more)

- What are you working on at the moment?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:43:14 - 00:45:25
- How can I turn a financial database I’m building into an interesting portfolio project to showcase work? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:45:25 - 00:49:23
- What advice do you have for data scientists who want to get into freelance/consulting? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:49:23 - 00:55:15
- What are important skills for DS beyond ML & AI? - Ask me anything! (data science, LLMs, landing a job, and more)

- What are important skills for DS beyond ML & AI?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:55:15 - 00:59:42
- Do I need to become a full-stack programmer to have success in this field? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
00:59:42 - 01:02:31
- If you weren’t allowed to do programming or create content, what would you do? - 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?

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
01:02:31 - 01:03:39
- How did you achieve your advanced height? Asking for a friend. - Ask me anything! (data science, LLMs, landing a job, and more)

- How did you achieve your advanced height? Asking for a friend.

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
01:03:39 - 01:04:23
- 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. - 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.

Ask me anything! (data science, LLMs, landing a job, and more)
2024年04月07日　
01:04:23 - 01:05:26
- Video Overview & Reference Material - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Video Overview & Reference Material

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:00:00 - 00:03:05
- Data & Code Setup - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Data & Code Setup

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:03:05 - 00:05:04
- Task #0: Configure LLM to use with Python (OpenAI API) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #0: Configure LLM to use with Python (OpenAI API)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:05:04 - 00:20:10
- Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:20:10 - 00:27:39
- Task #1: Use LLM to Parse Simple Sentence Examples - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #1: Use LLM to Parse Simple Sentence Examples

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:27:39 - 00:41:22
- Sub-task #1: Convert string to Python Object - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Sub-task #1: Convert string to Python Object

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:41:22 - 00:44:29
- Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:44:29 - 00:56:24
- Quick note on a benefit of using LangChain (easily switching between models) - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Quick note on a benefit of using LangChain (easily switching between models)

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:56:24 - 00:58:06
- Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
00:58:06 - 01:06:22
- Task #2: Connect Pages that Belong to the Same Documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #2: Connect Pages that Belong to the Same Documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
01:06:22 - 01:56:36
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 - 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

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　 @MaxwellSmi41483 様　
01:27:00 - 02:39:33
- Task #3: Parse out values from merged documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #3: Parse out values from merged documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
01:56:36 - 02:12:44
- Task #4 (setup): Analyze Results - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #4 (setup): Analyze Results

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
02:12:44 - 02:17:52
- Fixing up our results from task #3 quickly - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Fixing up our results from task #3 quickly

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
02:17:52 - 02:20:41
- Task #4: Find the average age of apprentices in our merged contract documents - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Task #4: Find the average age of apprentices in our merged contract documents

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
02:20:41 - 02:30:59
- Other analysis, wlho had the most apprentices? - Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)

- Other analysis, wlho had the most apprentices?

Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis)
2024年03月21日　
02:30:59 - 02:39:33
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