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Natural Language Processing

Learn NLP including text preprocessing, embeddings, sentiment analysis, and sequence-to-sequence models.

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Text Preprocessing

Learn how to prepare raw text data for NLP tasks.

Content by: Nirav Khanpara

AI/ML Engineer

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Why Preprocess Text?

Text preprocessing helps clean and standardize text, improving model accuracy.

Common Steps

•Lowercasing
•Removing punctuation
•Tokenization
•Stopword removal
•Stemming & Lemmatization

Implementation

Code Example

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

nltk.download('punkt')
nltk.download('stopwords')

text = "Natural Language Processing is fun and powerful!"
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in string.punctuation]
tokens = [t for t in tokens if t not in stopwords.words('english')]

print(tokens)

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import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

nltk.download('punkt')
nltk.download('stopwords')

text = "Natural Language Processing is fun and powerful!"
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in string.punctuation]
tokens = [t for t in tokens if t not in stopwords.words('english')]

print(tokens)

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🎯 Practice Exercise

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Word Embeddings

Understand word embeddings and their role in NLP.

Content by: Nirav Khanpara

AI/ML Engineer

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What are Word Embeddings?

Word embeddings map words into dense vectors, capturing semantic relationships.

Popular Techniques

•Word2Vec
•GloVe
•FastText
•Transformers (BERT embeddings)

Implementation with Gensim

Code Example

from gensim.models import Word2Vec

sentences = [["natural", "language", "processing"], ["machine", "learning", "is", "fun"]]
model = Word2Vec(sentences, vector_size=50, window=5, min_count=1, workers=4)

print(model.wv['natural'])

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from gensim.models import Word2Vec

sentences = [["natural", "language", "processing"], ["machine", "learning", "is", "fun"]]
model = Word2Vec(sentences, vector_size=50, window=5, min_count=1, workers=4)

print(model.wv['natural'])

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🎯 Practice Exercise

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Sentiment Analysis

Learn to classify text sentiment using deep learning.

Content by: Nirav Khanpara

AI/ML Engineer

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What is Sentiment Analysis?

A task to determine whether text expresses positive, negative, or neutral emotions.

Implementation with Keras

Code Example

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

sentences = ["I love NLP", "I hate spam emails"]
labels = np.array([1, 0])  # 1: Positive, 0: Negative

tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded = pad_sequences(sequences, maxlen=5)

model = keras.Sequential([
    keras.layers.Embedding(1000, 16, input_length=5),
    keras.layers.GlobalAveragePooling1D(),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded, labels, epochs=10, verbose=1)

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import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

sentences = ["I love NLP", "I hate spam emails"]
labels = np.array([1, 0])  # 1: Positive, 0: Negative

tokenizer = Tokenizer(num_words=1000)
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded = pad_sequences(sequences, maxlen=5)

model = keras.Sequential([
    keras.layers.Embedding(1000, 16, input_length=5),
    keras.layers.GlobalAveragePooling1D(),
    keras.layers.Dense(16, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded, labels, epochs=10, verbose=1)

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🎯 Practice Exercise

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Sequence-to-Sequence Models

Explore Seq2Seq models for translation and text generation.

Content by: Nirav Khanpara

AI/ML Engineer

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Applications

•Machine Translation
•Chatbots
•Text Summarization

Simple Seq2Seq with Keras

Code Example

from tensorflow import keras

encoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(encoder_inputs)
encoder_outputs, state_h, state_c = keras.layers.LSTM(64, return_state=True)(x)
encoder_states = [state_h, state_c]

decoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(decoder_inputs)
decoder_lstm = keras.layers.LSTM(64, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(x, initial_state=encoder_states)
decoder_dense = keras.layers.Dense(1000, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = keras.models.Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy')

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from tensorflow import keras

encoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(encoder_inputs)
encoder_outputs, state_h, state_c = keras.layers.LSTM(64, return_state=True)(x)
encoder_states = [state_h, state_c]

decoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(decoder_inputs)
decoder_lstm = keras.layers.LSTM(64, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(x, initial_state=encoder_states)
decoder_dense = keras.layers.Dense(1000, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = keras.models.Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy')

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🎯 Practice Exercise

Test your understanding of this topic:

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Previous Module

Module 5: Deep Learning & Neural Networks

Additional Resources

📚 Recommended Reading

•Speech and Language Processing by Jurafsky & Martin
•Natural Language Processing with Python (Bird, Klein, Loper)
•Deep Learning for NLP with PyTorch

🌐 Online Resources

•TensorFlow NLP Tutorials
•NLTK Documentation
•Hugging Face Transformers

Ready for the Next Module?

Continue your learning journey and master the next set of concepts.

Back to Course Overview

Module 6: Natural Language Processing

Natural Language Processing

Select Topics Overview

Text Preprocessing

Word Embeddings

Sentiment Analysis

Sequence-to-Sequence Models

Text Preprocessing

Word Embeddings

Sentiment Analysis

Sequence-to-Sequence Models

Text Preprocessing

Why Preprocess Text?

Common Steps

Implementation

🎯 Practice Exercise

Word Embeddings

What are Word Embeddings?

Popular Techniques

Implementation with Gensim

🎯 Practice Exercise

Sentiment Analysis

What is Sentiment Analysis?

Implementation with Keras

🎯 Practice Exercise

Sequence-to-Sequence Models

Applications

Simple Seq2Seq with Keras

🎯 Practice Exercise

Module 5: Deep Learning & Neural Networks

Additional Resources

📚 Recommended Reading

🌐 Online Resources

Ready for the Next Module?