π A Course in Natural Language Processing (2024)
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π Natural Language Understanding with Python (2024)
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π Natural Language Processing Practical (2022)
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ChatGPT cheat sheet for data science.pdf
29 MB
Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp
Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPTβs natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .
The guide also addresses ChatGPTβs limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.
Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL
https://t.iss.one/CodeProgrammerβοΈ
Source: DataCamp
Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPTβs natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .
The guide also addresses ChatGPTβs limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.
Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL
https://t.iss.one/CodeProgrammer
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The Big Book of Large Language Models by Damien Benveniste
β
Chapters:
1β£ Introduction
π’ Language Models Before Transformers
π’ Attention Is All You Need: The Original Transformer Architecture
π’ A More Modern Approach To The Transformer Architecture
π’ Multi-modal Large Language Models
π’ Transformers Beyond Language Models
π’ Non-Transformer Language Models
π’ How LLMs Generate Text
π’ From Words To Tokens
1β£ 0β£ Training LLMs to Follow Instructions
1β£ 1β£ Scaling Model Training
1β£ π’ Fine-Tuning LLMs
1β£ π’ Deploying LLMs
Read it: https://book.theaiedge.io/
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
https://t.iss.one/CodeProgrammer
Read it: https://book.theaiedge.io/
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
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π¨π»βπ» If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
https://t.iss.one/CodeProgrammer
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The Hundred-Page Language Models Book
Read it:
https://github.com/aburkov/theLMbook
Read it:
https://github.com/aburkov/theLMbook
#LLM #NLP #ML #AI #PYTHON #PYTORCH
https://t.iss.one/DataScienceM
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The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.
The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.
The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available
.
All slides, #code and additional materials can be found at the link provided.
π Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Python | Machine Learning | Coding | R
Foundations of Large Language Models
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper
π5β€1
Forwarded from Python | Machine Learning | Coding | R
Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".
https://www.k-a.in/pyt-transformer.html
This guide offers:
By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.
#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworksο»Ώ
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Full PyTorch Implementation of Transformer-XL
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://t.iss.one/CodeProgrammer
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://t.iss.one/CodeProgrammer
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A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:
π Positive sentiment
βΉοΈ Negative sentiment
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
π GitHub: https://lnkd.in/e_gk3hfe
π° Article: https://lnkd.in/e_baNJd2
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
π Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
π± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
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Forwarded from Python | Machine Learning | Coding | R
Python Cheat Sheet
β‘οΈ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
π± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#AI #SentimentAnalysis #DataVisualization #pandas #Numpy #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
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Forwarded from Python | Machine Learning | Coding | R
LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
βοΈ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBkπ± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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β€4π2
Topic: RNN (Recurrent Neural Networks) β Part 1 of 4: Introduction and Core Concepts
---
1. What is an RNN?
β’ A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data, such as time series, text, or speech.
β’ Unlike feedforward networks, RNNs maintain a memory of previous inputs using hidden states, which makes them powerful for tasks with temporal dependencies.
---
2. How RNNs Work
β’ RNNs process one element of the sequence at a time while maintaining an internal hidden state.
β’ The hidden state is updated at each time step and used along with the current input to predict the next output.
$$
h_t = \tanh(W_h h_{t-1} + W_x x_t + b)
$$
Where:
β’ $x_t$ = input at time step t
β’ $h_t$ = hidden state at time t
β’ $W_h, W_x$ = weight matrices
β’ $b$ = bias
---
3. Applications of RNNs
β’ Text classification
β’ Language modeling
β’ Sentiment analysis
β’ Time-series prediction
β’ Speech recognition
β’ Machine translation
---
4. Basic RNN Architecture
β’ Input layer: Sequence of data (e.g., words or time points)
β’ Recurrent layer: Applies the same weights across all time steps
β’ Output layer: Generates prediction (either per time step or overall)
---
5. Simple RNN Example in PyTorch
---
6. Summary
β’ RNNs are effective for sequential data due to their internal memory.
β’ Unlike CNNs or FFNs, RNNs take time dependency into account.
β’ PyTorch offers built-in RNN modules for easy implementation.
---
Exercise
β’ Build an RNN to predict the next character in a short string of text (e.g., βhelloβ).
---
#RNN #DeepLearning #SequentialData #TimeSeries #NLP
https://t.iss.one/DataScienceM
---
1. What is an RNN?
β’ A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data, such as time series, text, or speech.
β’ Unlike feedforward networks, RNNs maintain a memory of previous inputs using hidden states, which makes them powerful for tasks with temporal dependencies.
---
2. How RNNs Work
β’ RNNs process one element of the sequence at a time while maintaining an internal hidden state.
β’ The hidden state is updated at each time step and used along with the current input to predict the next output.
$$
h_t = \tanh(W_h h_{t-1} + W_x x_t + b)
$$
Where:
β’ $x_t$ = input at time step t
β’ $h_t$ = hidden state at time t
β’ $W_h, W_x$ = weight matrices
β’ $b$ = bias
---
3. Applications of RNNs
β’ Text classification
β’ Language modeling
β’ Sentiment analysis
β’ Time-series prediction
β’ Speech recognition
β’ Machine translation
---
4. Basic RNN Architecture
β’ Input layer: Sequence of data (e.g., words or time points)
β’ Recurrent layer: Applies the same weights across all time steps
β’ Output layer: Generates prediction (either per time step or overall)
---
5. Simple RNN Example in PyTorch
import torch
import torch.nn as nn
class BasicRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(BasicRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.rnn(x) # out: [batch, seq_len, hidden]
out = self.fc(out[:, -1, :]) # Take the output from last time step
return out
---
6. Summary
β’ RNNs are effective for sequential data due to their internal memory.
β’ Unlike CNNs or FFNs, RNNs take time dependency into account.
β’ PyTorch offers built-in RNN modules for easy implementation.
---
Exercise
β’ Build an RNN to predict the next character in a short string of text (e.g., βhelloβ).
---
#RNN #DeepLearning #SequentialData #TimeSeries #NLP
https://t.iss.one/DataScienceM
β€7
Topic: RNN (Recurrent Neural Networks) β Part 2 of 4: Types of RNNs and Architectural Variants
---
1. Vanilla RNN β Limitations
β’ Standard (vanilla) RNNs suffer from vanishing gradients and short-term memory.
β’ As sequences get longer, it becomes difficult for the model to retain long-term dependencies.
---
2. Types of RNN Architectures
β’ One-to-One
Example: Image Classification
A single input and a single output.
β’ One-to-Many
Example: Image Captioning
A single input leads to a sequence of outputs.
β’ Many-to-One
Example: Sentiment Analysis
A sequence of inputs gives one output (e.g., sentiment score).
β’ Many-to-Many
Example: Machine Translation
A sequence of inputs maps to a sequence of outputs.
---
3. Bidirectional RNNs (BiRNNs)
β’ Process the input sequence in both forward and backward directions.
β’ Allow the model to understand context from both past and future.
---
4. Deep RNNs (Stacked RNNs)
β’ Multiple RNN layers stacked on top of each other.
β’ Capture more complex temporal patterns.
---
5. RNN with Different Output Strategies
β’ Last Hidden State Only:
Use the final output for classification/regression.
β’ All Hidden States:
Use all time-step outputs, useful in sequence-to-sequence models.
---
6. Example: Many-to-One RNN in PyTorch
---
7. Summary
β’ RNNs can be adapted for different tasks: one-to-many, many-to-one, etc.
β’ Bidirectional and stacked RNNs enhance performance by capturing richer patterns.
β’ It's important to choose the right architecture based on the sequence problem.
---
Exercise
β’ Modify the RNN model to use bidirectional layers and evaluate its performance on a text classification dataset.
---
#RNN #BidirectionalRNN #DeepLearning #TimeSeries #NLP
https://t.iss.one/DataScienceM
---
1. Vanilla RNN β Limitations
β’ Standard (vanilla) RNNs suffer from vanishing gradients and short-term memory.
β’ As sequences get longer, it becomes difficult for the model to retain long-term dependencies.
---
2. Types of RNN Architectures
β’ One-to-One
Example: Image Classification
A single input and a single output.
β’ One-to-Many
Example: Image Captioning
A single input leads to a sequence of outputs.
β’ Many-to-One
Example: Sentiment Analysis
A sequence of inputs gives one output (e.g., sentiment score).
β’ Many-to-Many
Example: Machine Translation
A sequence of inputs maps to a sequence of outputs.
---
3. Bidirectional RNNs (BiRNNs)
β’ Process the input sequence in both forward and backward directions.
β’ Allow the model to understand context from both past and future.
nn.RNN(input_size, hidden_size, bidirectional=True)
---
4. Deep RNNs (Stacked RNNs)
β’ Multiple RNN layers stacked on top of each other.
β’ Capture more complex temporal patterns.
nn.RNN(input_size, hidden_size, num_layers=2)
---
5. RNN with Different Output Strategies
β’ Last Hidden State Only:
Use the final output for classification/regression.
β’ All Hidden States:
Use all time-step outputs, useful in sequence-to-sequence models.
---
6. Example: Many-to-One RNN in PyTorch
import torch.nn as nn
class SentimentRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SentimentRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
out, _ = self.rnn(x)
final_out = out[:, -1, :] # Get the last time-step output
return self.fc(final_out)
---
7. Summary
β’ RNNs can be adapted for different tasks: one-to-many, many-to-one, etc.
β’ Bidirectional and stacked RNNs enhance performance by capturing richer patterns.
β’ It's important to choose the right architecture based on the sequence problem.
---
Exercise
β’ Modify the RNN model to use bidirectional layers and evaluate its performance on a text classification dataset.
---
#RNN #BidirectionalRNN #DeepLearning #TimeSeries #NLP
https://t.iss.one/DataScienceM
π₯2
Topic: RNN (Recurrent Neural Networks) β Part 4 of 4: Advanced Techniques, Training Tips, and Real-World Use Cases
---
1. Advanced RNN Variants
β’ Bidirectional LSTM/GRU: Processes the sequence in both forward and backward directions, improving context understanding.
β’ Stacked RNNs: Uses multiple layers of RNNs to capture complex patterns at different levels of abstraction.
---
2. Sequence-to-Sequence (Seq2Seq) Models
β’ Used in tasks like machine translation, chatbots, and text summarization.
β’ Consist of two RNNs:
* Encoder: Converts input sequence to a context vector
* Decoder: Generates output sequence from the context
---
3. Attention Mechanism
β’ Solves the bottleneck of relying only on the final hidden state in Seq2Seq.
β’ Allows the decoder to focus on relevant parts of the input sequence at each step.
---
4. Best Practices for Training RNNs
β’ Gradient Clipping: Prevents exploding gradients by limiting their values.
β’ Batching with Padding: Sequences in a batch must be padded to equal length.
β’ Packed Sequences: Efficient way to handle variable-length sequences in PyTorch.
---
5. Real-World Use Cases of RNNs
β’ Speech Recognition β Converting audio into text.
β’ Language Modeling β Predicting the next word in a sequence.
β’ Financial Forecasting β Predicting stock prices or sales trends.
β’ Healthcare β Predicting patient outcomes based on sequential medical records.
---
6. Combining RNNs with Other Models
β’ RNNs can be combined with CNNs for tasks like video classification (CNN for spatial, RNN for temporal features).
β’ Used with transformers in hybrid models for specialized NLP tasks.
---
Summary
β’ Advanced RNN techniques like attention, bidirectionality, and stacked layers make RNNs powerful for complex tasks.
β’ Proper training strategies like gradient clipping and sequence packing are essential for performance.
---
Exercise
β’ Build a Seq2Seq model with attention for English-to-French translation using an LSTM encoder-decoder in PyTorch.
---
#RNN #Seq2Seq #Attention #DeepLearning #NLP
https://t.iss.one/DataScience4M
---
1. Advanced RNN Variants
β’ Bidirectional LSTM/GRU: Processes the sequence in both forward and backward directions, improving context understanding.
β’ Stacked RNNs: Uses multiple layers of RNNs to capture complex patterns at different levels of abstraction.
nn.LSTM(input_size, hidden_size, num_layers=2, bidirectional=True)
---
2. Sequence-to-Sequence (Seq2Seq) Models
β’ Used in tasks like machine translation, chatbots, and text summarization.
β’ Consist of two RNNs:
* Encoder: Converts input sequence to a context vector
* Decoder: Generates output sequence from the context
---
3. Attention Mechanism
β’ Solves the bottleneck of relying only on the final hidden state in Seq2Seq.
β’ Allows the decoder to focus on relevant parts of the input sequence at each step.
---
4. Best Practices for Training RNNs
β’ Gradient Clipping: Prevents exploding gradients by limiting their values.
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
β’ Batching with Padding: Sequences in a batch must be padded to equal length.
β’ Packed Sequences: Efficient way to handle variable-length sequences in PyTorch.
packed_input = nn.utils.rnn.pack_padded_sequence(input, lengths, batch_first=True)
---
5. Real-World Use Cases of RNNs
β’ Speech Recognition β Converting audio into text.
β’ Language Modeling β Predicting the next word in a sequence.
β’ Financial Forecasting β Predicting stock prices or sales trends.
β’ Healthcare β Predicting patient outcomes based on sequential medical records.
---
6. Combining RNNs with Other Models
β’ RNNs can be combined with CNNs for tasks like video classification (CNN for spatial, RNN for temporal features).
β’ Used with transformers in hybrid models for specialized NLP tasks.
---
Summary
β’ Advanced RNN techniques like attention, bidirectionality, and stacked layers make RNNs powerful for complex tasks.
β’ Proper training strategies like gradient clipping and sequence packing are essential for performance.
---
Exercise
β’ Build a Seq2Seq model with attention for English-to-French translation using an LSTM encoder-decoder in PyTorch.
---
#RNN #Seq2Seq #Attention #DeepLearning #NLP
https://t.iss.one/DataScience4M
Topic: Handling Datasets of All Types β Part 4 of 5: Text Data Processing and Natural Language Processing (NLP)
---
1. Understanding Text Data
β’ Text data is unstructured and requires preprocessing to convert into numeric form for ML models.
β’ Common tasks: classification, sentiment analysis, language modeling.
---
2. Text Preprocessing Steps
β’ Tokenization: Splitting text into words or subwords.
β’ Lowercasing: Convert all text to lowercase for uniformity.
β’ Removing Punctuation and Stopwords: Clean unnecessary words.
β’ Stemming and Lemmatization: Reduce words to their root form.
---
3. Encoding Text Data
β’ Bag-of-Words (BoW): Represents text as word count vectors.
β’ TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.
β’ Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).
---
4. Loading and Processing Text Data in Python
---
5. Handling Large Text Datasets
β’ Use libraries like NLTK, spaCy, and Transformers.
β’ For deep learning, tokenize using models like BERT or GPT.
---
6. Summary
β’ Text data needs extensive preprocessing and encoding.
β’ Choosing the right representation is crucial for model success.
---
Exercise
β’ Clean a set of sentences by tokenizing and removing stopwords.
β’ Convert cleaned text into TF-IDF vectors.
---
#NLP #TextProcessing #DataScience #MachineLearning #Python
https://t.iss.one/DataScienceM
---
1. Understanding Text Data
β’ Text data is unstructured and requires preprocessing to convert into numeric form for ML models.
β’ Common tasks: classification, sentiment analysis, language modeling.
---
2. Text Preprocessing Steps
β’ Tokenization: Splitting text into words or subwords.
β’ Lowercasing: Convert all text to lowercase for uniformity.
β’ Removing Punctuation and Stopwords: Clean unnecessary words.
β’ Stemming and Lemmatization: Reduce words to their root form.
---
3. Encoding Text Data
β’ Bag-of-Words (BoW): Represents text as word count vectors.
β’ TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.
β’ Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).
---
4. Loading and Processing Text Data in Python
from sklearn.feature_extraction.text import TfidfVectorizer
texts = ["I love data science.", "Data science is fun."]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
---
5. Handling Large Text Datasets
β’ Use libraries like NLTK, spaCy, and Transformers.
β’ For deep learning, tokenize using models like BERT or GPT.
---
6. Summary
β’ Text data needs extensive preprocessing and encoding.
β’ Choosing the right representation is crucial for model success.
---
Exercise
β’ Clean a set of sentences by tokenizing and removing stopwords.
β’ Convert cleaned text into TF-IDF vectors.
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#NLP #TextProcessing #DataScience #MachineLearning #Python
https://t.iss.one/DataScienceM
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