- Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification(00:42:05 - 00:47:50) - Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

- Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification(00:42:05 - 00:47:50)
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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---------------------------

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
- 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

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。

Timetable

動画タイムテーブル

動画数:84件

- 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
- Introduction - How to make your GitHub more impressive to Employers! (5 simple tips)

- Introduction

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:00:00 - 00:01:50
- Tip 1: Show Private Repository Activity - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 1: Show Private Repository Activity

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:01:50 - 00:02:57
- Tip 2: Highlight best work using pins - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 2: Highlight best work using pins

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:02:57 - 00:04:13
- Tip 3: Create a Profile README - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 3: Create a Profile README

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:04:13 - 00:11:32
- Tip 4: Fill in all Profile Details - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 4: Fill in all Profile Details

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:11:32 - 00:13:45
- Tip 5: Fill in READMEs on highlighted repos - How to make your GitHub more impressive to Employers! (5 simple tips)

- Tip 5: Fill in READMEs on highlighted repos

How to make your GitHub more impressive to Employers! (5 simple tips)
2024年02月27日 
00:13:45 - 00:19:01
- Overview & Getting Started - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- Overview & Getting Started

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:00:00 - 00:00:50
- 1. Predicting Heart Attack Risk (Easy Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 1. Predicting Heart Attack Risk (Easy Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:00:50 - 00:06:44
- 2. Data Anonymization (Medium Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 2. Data Anonymization (Medium Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:06:44 - 00:11:53
- 3.  Dessert Duel (Hard Problem) - Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)

- 3. Dessert Duel (Hard Problem)

Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas)
2023年12月27日 
00:11:53 - 00:29:59
- Overview - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Overview

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:00:00 - 00:00:41
- Getting started on the Book Review API - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Getting started on the Book Review API

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:00:41 - 00:02:20
- Set up Airtable as our database & connect to it with Python - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Set up Airtable as our database & connect to it with Python

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:02:20 - 00:10:44
- Implement code to add reviews and view all reviews - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Implement code to add reviews and view all reviews

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:10:44 - 00:31:40
- Adding a POST request to our API - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Adding a POST request to our API

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:31:40 - 00:36:40
- Trying out our new endpoints (using documentation & requests library of Python) - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Trying out our new endpoints (using documentation & requests library of Python)

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:36:40 - 00:41:32
- Commit code to Github & deploy live to Render.com - Python Project: Implement a REST API with Flask & Flasgger Libraries!

- Commit code to Github & deploy live to Render.com

Python Project: Implement a REST API with Flask & Flasgger Libraries!
2023年12月04日 
00:41:32 - 00:50:46
- Video overview - How to create & deploy an API in Python! (with interactive documentation)

- Video overview

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:00:00 - 00:01:18
- What we're building - How to create & deploy an API in Python! (with interactive documentation)

- What we're building

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:01:18 - 00:03:20
- How to get setup with Github template code - How to create & deploy an API in Python! (with interactive documentation)

- How to get setup with Github template code

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:03:20 - 00:07:00
- Taking a look at the Flask, Flasgger Python3 code - How to create & deploy an API in Python! (with interactive documentation)

- Taking a look at the Flask, Flasgger Python3 code

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:07:00 - 00:08:38
- Testing some API requests (GET) locally - How to create & deploy an API in Python! (with interactive documentation)

- Testing some API requests (GET) locally

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:08:38 - 00:13:09
- Building another GET request endpoint (with multiple parameters) - How to create & deploy an API in Python! (with interactive documentation)

- Building another GET request endpoint (with multiple parameters)

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:13:09 - 00:14:34
- Using ChatGPT to help us build another endpoint - How to create & deploy an API in Python! (with interactive documentation)

- Using ChatGPT to help us build another endpoint

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:14:34 - 00:22:43
- Deploying our API to a live public URL endpoint (using render.com) - How to create & deploy an API in Python! (with interactive documentation)

- Deploying our API to a live public URL endpoint (using render.com)

How to create & deploy an API in Python! (with interactive documentation)
2023年11月26日 
00:22:43 - 00:29:27
- Video overview & topics covered - Complete Regular Expressions Tutorial! (with exercises for practice)

- Video overview & topics covered

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:00:00 - 00:01:43
- Basic regex syntax (building up an intuition) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Basic regex syntax (building up an intuition)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:01:43 - 00:04:23
- Character Sets Overview ([A-Za-z0-9]) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Character Sets Overview ([A-Za-z0-9])

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:04:23 - 00:05:57
- Quantifiers Guide (*, +, ?, {3,5}) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Quantifiers Guide (*, +, ?, {3,5})

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:05:57 - 00:09:30
- Guided Exercise: Find all words that don't use vowels - Complete Regular Expressions Tutorial! (with exercises for practice)

- Guided Exercise: Find all words that don't use vowels

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:09:30 - 00:11:08
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. 🤓 - 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. 🤓

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Anon Viewer 様 
00:10:50 - 00:36:40
- Helpful cheat sheet to remember regex syntax in the real-world - Complete Regular Expressions Tutorial! (with exercises for practice)

- Helpful cheat sheet to remember regex syntax in the real-world

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:11:08 - 00:12:47
- Matching words/patterns of a specific length ({3,5}) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Matching words/patterns of a specific length ({3,5})

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:12:47 - 00:14:58
- OR operator overview - Complete Regular Expressions Tutorial! (with exercises for practice)

- OR operator overview

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:14:58 - 00:17:14
- Guided Exercise: Match valid sentences (starts with capital letter, ends with period) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Guided Exercise: Match valid sentences (starts with capital letter, ends with period)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:17:14 - 00:21:18
- Character classes overview (\w, \b, \d, \s) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Character classes overview (\w, \b, \d, \s)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:21:18 - 00:23:13
- Escaping Characters - Complete Regular Expressions Tutorial! (with exercises for practice)

- Escaping Characters

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:23:13 - 00:25:02
- Practice Exercise #1: Write a regular expression to match meme text format - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #1: Write a regular expression to match meme text format

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:25:02 - 00:30:39
- Practice Exercise #2: Write a regular expression to match a specific date format - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #2: Write a regular expression to match a specific date format

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:30:39 - 00:39:03
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. ▶ - 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. ▶

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Anon Viewer 様 
00:36:40 - 01:19:21
- Groups overview - Complete Regular Expressions Tutorial! (with exercises for practice)

- Groups overview

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:39:03 - 00:50:16
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+) - 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+)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日  Dendrocnide Moroides 様 
00:49:02 - 01:19:21
- Lookahead & Lookbehind Assertions - Complete Regular Expressions Tutorial! (with exercises for practice)

- Lookahead & Lookbehind Assertions

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
00:50:16 - 01:00:18
- Practice Exercise #3: Detect if same word pops up multiple times in a sentence - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #3: Detect if same word pops up multiple times in a sentence

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:00:18 - 01:06:04
- Practice Exercise #4: Password matching with rules - Complete Regular Expressions Tutorial! (with exercises for practice)

- Practice Exercise #4: Password matching with rules

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:06:04 - 01:16:16
- Some final recommendations! (additional practice, chatgpt, etc.) - Complete Regular Expressions Tutorial! (with exercises for practice)

- Some final recommendations! (additional practice, chatgpt, etc.)

Complete Regular Expressions Tutorial! (with exercises for practice)
2023年04月13日 
01:16:16 - 01:19:21
- Video overview & format - Full Data Science Mock Interview! (featuring Kylie Ying)

- Video overview & format

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:00:00 - 00:03:38
- Introductory Behavioral questions | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Introductory Behavioral questions | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:03:38 - 00:09:11
- Social media platform bot issue task overview | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Social media platform bot issue task overview | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:09:11 - 00:16:51
- What are some features we should investigate regarding the bot issue? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- What are some features we should investigate regarding the bot issue? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:16:51 - 00:26:27
- Classification model implementation details (using feature vectors) | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Classification model implementation details (using feature vectors) | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:26:27 - 00:43:03
- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:43:03 - 00:53:03
- Technical implementation details (python libraries, cloud services, etc) | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Technical implementation details (python libraries, cloud services, etc) | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:53:03 - 00:57:26
- Any questions for me? | Data science interview - Full Data Science Mock Interview! (featuring Kylie Ying)

- Any questions for me? | Data science interview

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
00:57:26 - 01:05:07
- Post-interview breakdown & analysis - Full Data Science Mock Interview! (featuring Kylie Ying)

- Post-interview breakdown & analysis

Full Data Science Mock Interview! (featuring Kylie Ying)
2023年01月09日 
01:05:07 - 01:27:34
- Video Introduction - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Video Introduction

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:00:00 - 00:01:19
- How podcasts work (RSS feeds overview) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- How podcasts work (RSS feeds overview)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:01:19 - 00:05:11
- How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:05:11 - 00:07:47
- Accessing this project on GitHub - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Accessing this project on GitHub

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:07:47 - 00:09:22
-Writing Python code to download podcasts locally (requests & beautifulsoup libraries) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

-Writing Python code to download podcasts locally (requests & beautifulsoup libraries)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:09:22 - 00:18:10
- Modify our script to be able to download many podcasts - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Modify our script to be able to download many podcasts

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:18:10 - 00:22:51
- Building in smart search capabilities to grab podcasts we’ll find most interesting! - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Building in smart search capabilities to grab podcasts we’ll find most interesting!

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:22:51 - 00:31:00
- Using the AssemblyAI API to transcribe the podcasts we’ve downloaded - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Using the AssemblyAI API to transcribe the podcasts we’ve downloaded

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
00:31:00 - 01:06:08
- Cleaning our code with functions & classes and putting everything into Python scripts. - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Cleaning our code with functions & classes and putting everything into Python scripts.

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:06:08 - 01:18:09
- Portfolio project extension ideas! (Spotify API, NLP semantic search) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Portfolio project extension ideas! (Spotify API, NLP semantic search)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:18:09 - 01:19:56
- Smash like & subscribe pretty please :) - Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.

- Smash like & subscribe pretty please :)

Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts.
2022年11月23日 
01:19:56 - 01:20:39
- Intro & Video Overview - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Intro & Video Overview

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:00:00 - 00:00:46
- Check out this Video’s Sponsor, Brilliant! - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Check out this Video’s Sponsor, Brilliant!

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:00:46 - 00:03:10
- Coding #1 (Microsoft, Easy) - Finding Updated Records - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #1 (Microsoft, Easy) - Finding Updated Records

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:03:10 - 00:10:36
- Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:10:36 - 00:16:38
- Coding #3 (Google, Medium) - Counting Instances in Text - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #3 (Google, Medium) - Counting Instances in Text

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:16:38 - 00:28:23
I know it's more a reference to the stock market terms, but I can't stop thinking of Fallout: New Vegas. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

I know it's more a reference to the stock market terms, but I can't stop thinking of Fallout: New Vegas.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:17:20 - 01:11:00
- Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:28:23 - 00:36:51
That first one and others are SQL problems converted to pandas. I suppose that's a decent way to get basic pd questions. () - Solving Real-World Data Science Interview Questions! (with Python Pandas)

That first one and others are SQL problems converted to pandas. I suppose that's a decent way to get basic pd questions. ()

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:28:48 - 00:17:20
- Coding #5 (Amazon, Hard) - Monthly Percentage Difference - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #5 (Amazon, Hard) - Monthly Percentage Difference

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:36:51 - 00:56:38
AtI work for Amazon's RPA team, trying to make a career in data science. Last month I was appearing for an IJP and got the same question in SQL coding round.Thanks for making this Keith. Keep them coming. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

AtI work for Amazon's RPA team, trying to make a career in data science. Last month I was appearing for an IJP and got the same question in SQL coding round.Thanks for making this Keith. Keep them coming.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:37:48 - 01:47:50
- Coding #6 (Microsoft, Hard) - Premium vs Freemium - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Coding #6 (Microsoft, Hard) - Premium vs Freemium

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
00:56:38 - 01:10:28
- Non-Coding #1 (Visa, Easy) - Credit Card Activity - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #1 (Visa, Easy) - Credit Card Activity

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:10:28 - 01:13:33
If you have the locations that's just a simple matter of putting it on a map and seeing where it clusters the most. - Solving Real-World Data Science Interview Questions! (with Python Pandas)

If you have the locations that's just a simple matter of putting it on a map and seeing where it clusters the most.

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:11:00 - 01:28:00
- Non-Coding #2 (IBM, Easy) - Outliers Detection - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #2 (IBM, Easy) - Outliers Detection

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:13:33 - 01:16:46
- Non-Coding #3 (Google, Medium) - Probability of Having a Sister - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #3 (Google, Medium) - Probability of Having a Sister

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:16:46 - 01:27:19
- Non-Coding #4 (Uber, Medium) - Uber Black Rides - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #4 (Uber, Medium) - Uber Black Rides

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:27:19 - 01:36:57
Context, context, context. Was that the only reduction? - Solving Real-World Data Science Interview Questions! (with Python Pandas)

Context, context, context. Was that the only reduction?

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:28:00 - 01:47:50
- Non-Coding #5 (Capital One, Hard) - Terabyte of Data - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Non-Coding #5 (Capital One, Hard) - Terabyte of Data

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:36:57 - 01:46:41
- Video Conclusion & Recap - Solving Real-World Data Science Interview Questions! (with Python Pandas)

- Video Conclusion & Recap

Solving Real-World Data Science Interview Questions! (with Python Pandas)
2022年07月26日 
01:46:41 - 01:47:50