conclusion: parquet file is the best regarding storage size and write ,read speed

Read the docs. Enumerate() does exactly the same thing you did in the other examples.

There are numerous reasons why this does not make any sense. Don't follow a guy like this.

this video was very helpful for me (new to linux and data science)

It is really a wonderful video! I just wonder @Rob, do we need to do Cross Validations? Are there any hyper-parameters that we also need to optimise? How to do the cross validations here in the NLP? Just like the normal ML Cross Validation process? Should we worry about the overfitting under-fittings problems? How would the learning curve look like with and without the cross validations? Thanks

Super helpful!

amazing!!

Did you compare the speed of the query method vs. df[ (df.col1>=value) & (df.col2 <= value)] ? I did.

thank you, Rob!

Mordecai???

Y do u prob look like pigmie? 0:04

POV: U've learned a knew thing & excited 2 teach ppl abt it...

How is it with respect to data privacy?Does it store our data?

Can we find the flights.parguet from kaggle?

Can you make dsa problem and solution video

Can you make dsa problem and solution video

Can you make dsa problem and solution video

Can you make dsa problem and solution video

Can someone help me with what the “thing” means

As soon as you said it I blurted out ENUMERATE, still watched to the end to see if you came up with something better tho lol

Please also provide the code walkthrough

Excellent and elegant flow of concepts and implementatiom.

great video mate

which ide is it?

Is their anything for Android phones?

amazing it helps me a lot! , the last part about quering on pandas was confused, do you have a video where you explain quering on pandas?

🎯 Key points for quick navigation: 00:00 *🎬 Introduction to Sentiment Analysis* - Introduction to natural language processing (NLP) and sentiment analysis. - Overview of the project, including using traditional techniques like VADER and more advanced models like RoBERTa. - Explanation of the dataset used for sentiment analysis, which consists of Amazon food reviews with ratings. 03:00 *📊 Data Preprocessing and Exploration* - Importing necessary libraries for data analysis and visualization. - Reading the dataset and performing basic exploratory data analysis (EDA). - Downsampling the dataset for quicker analysis and showcasing the structure of the data. 05:05 *📈 Exploring Sentiment Distribution* - Analyzing the distribution of sentiment scores based on review ratings. - Visualizing the distribution of sentiment scores across different star ratings using bar plots. - Observing the relationship between review ratings and sentiment scores. 07:00 *🧠 Introduction to NLTK for Sentiment Analysis* - Overview of NLTK (Natural Language Toolkit) and its capabilities for text processing. - Demonstrating tokenization and part-of-speech tagging using NLTK. - Explaining the process of chunking text into entities using NLTK. 10:48 *📉 Sentiment Analysis with VADER* - Introduction to VADER (Valence Aware Dictionary and sEntiment Reasoner) for sentiment analysis. - Understanding how VADER assigns sentiment scores based on individual words. - Applying VADER sentiment analysis to example sentences and the food review dataset. 23:41 *🔍 Advanced Sentiment Analysis with RoBERTa* - Introducing RoBERTa, a transformer-based deep learning model for contextual understanding. - Preprocessing text and encoding it for analysis using RoBERTa's tokenizer. - Applying the pre-trained RoBERTa model to perform sentiment analysis on text data. 29:05 *📊 Comparing Vader and Roberta sentiment analysis models* - Demonstrated how to print scores from both Vader and Roberta sentiment analysis models. - Created a scores dictionary for both models to store negative, neutral, and positive scores. - Illustrated the difference in sentiment analysis results between the Vader and Roberta models using a negative review as an example. 35:52 *📈 Comparing sentiment scores across models and reviewing examples* - Utilized Seaborn's pair plot to compare sentiment scores between Vader and Roberta models. - Reviewed examples where the sentiment analysis model contradicted the actual review sentiment, showcasing nuances in language understanding. - Examined instances where both models misinterpreted the sentiment of reviews, highlighting the limitations of bag-of-words approaches like Vader. 42:08 *🤖 Simplifying sentiment analysis with Hugging Face Transformers pipeline* - Demonstrated how to use Hugging Face Transformers pipeline for sentiment analysis, simplifying the process to just two lines of code. - Showcased the ease of changing models and tokenizers within the pipeline for different analysis tasks. - Provided examples of sentiment analysis using the pipeline, showcasing its efficiency and accuracy. Made with HARPA AI

It's really hard to find good videos on this topic. This was fantastic. Thank you.

Actually, for seeing what type it is, or data has how much volume, etc... for my daily practice I prefer df.info() than split it into df.shape, df.dtypes But for the rest I appreciate this workload, thanks so much!!

Source code please

Eat brains😂😂😂😂

Thanks for your tutorial. But I have a question. This is my code example: import numpy as np import cv2 import matplotlib.pyplot as plt img = cv2.imread('D:/CCU-2024/grasppose/p1.jpg',0) plt.figure() plt.subplot(1,2,1) plt.imshow(img) plt.show(). So, when I move the mouse on the image of plt.imshow(). I obtain the (x,y) and [a]. So what is (x,y) ? it's (u,v) right? and [a] is pixel value, right?

quite useful 👋

hello mr rob i get value and name error at tokeneizer line after installation of tokeneizer could you please help me for this

This video claims that these “noob” situations happen a lot. the enumerate function is one of the basics every tutorial never misses. The reason being is that this is one way to do a for-loop in an “OOP-style”. The real noobs are the ones that don’t read the docs.

fingers are considered as carrots - that was the most impressive AI humour :)

As someone just getting introduced to time series analysis, this video was gold, thank you for making it!

I am that double noob. I'm subscribing right away!

Is there any way i can remove the back ground music

Tbh i didnt know this i always tock average to equal mean

Very good explanation . Thanks a lot❤❤

@Rob Mulla: It is requested to make comprehensive playlists on "Data Analytics & Visualization" using pandas, polars, matplotlib, seaborn, numpy etc. and how to connect with different databases from VS or Jupyter, and also REGEX.

Can u tell me where u execute ur code/ How do I get to the same terminal

is this open source? Impressive.

😂😂

This is super cool, I love it❤. I'm also a youtuber with the new channel about AI and tech reviews. I will be watching your content.

is there anyone have an idea how to extract table data from image by maintaining the table format

Then I am "Pro Noob", because I use any of these methods at will 😂😂😂

As a data analyst I learn many things from you❤

Not representing Insightface or any beneficiary. But be careful about their usage licence - it is tricky. So if anyone gives tutorial video - it is ok. But if monetised - not ok. If you use image for education (what one will do with it - images ! S_x education) - it is ok. If you sell those images - you are not ok. Code is free. Outcome is not...lol... "The code of InsightFace is released under the MIT License. There is no limitation for both academic and commercial usage. The training data containing the annotation (and the models trained with these data) are available for non-commercial research purposes only. Both manual-downloading models from our github repo and auto-downloading models with our python-library follow the above license policy(which is for non-commercial research purposes only)."