## Week 1: Getting Ready for NLP & Preprocessing
### 📅 **DAY 1: Getting an overview**

First of all, check out [this](https://youtube.com/playlist?list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&si=TCo4_WRRfNEwUkIY) amazing playlist by **TensorFlow** to get a crisp idea of what we are going to do this week. It's packed with insightful videos that will lay a solid foundation for your NLP journey!

[](https://youtube.com/playlist?list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S&si=TCo4_WRRfNEwUkIY)

### 📅 DAY 2: Kickstarting with Text Preprocessing, Normalization, Regular Expressions, and Edit Distance

#### a. **Text Preprocessing: The Foundation of NLP**
Imagine trying to read a messy, smudged book – not fun, right? Text preprocessing is like cleaning up that book, making it crisp and readable. It transforms chaotic, noisy text into a tidy format that's ready for analysis. This process is crucial because cleaner data leads to better NLP model performance!

🔍 **Explore More:**
Dive deeper into text preprocessing [**here**](https://ayselaydin.medium.com/1-text-preprocessing-techniques-for-nlp-37544483c007).

#### b. **Text Normalization: Streamlining Your Data**
Think of text normalization as decluttering your digital workspace. It standardizes text, eliminating noise and focusing on the essence. This involves steps like converting text to lowercase, removing punctuation, and applying techniques like lemmatization and stemming. Essentially, it's text preprocessing on steroids!

🔍 **Discover the Magic of Normalization:**
Learn more about normalization [**here**](https://towardsdatascience.com/text-normalization-for-natural-language-processing-nlp-70a314bfa646).

#### c. **Regular Expressions: Your Pattern Detective**
Regular expressions (regex) are like the ultimate search-and-replace tool, but on steroids! They help you find patterns in text – imagine being able to pinpoint every email address or phone number in a document with a simple command.

🎥 **Watch and Learn:**
Check out this video on regex [**here**](https://youtu.be/a5i8RHodh00?si=VgWsWscMfJhEB6aX).

#### d. **Edit Distance: Measuring Textual Similarity**
Ever wondered how similar two strings are? Edit distance, specifically Levenshtein distance, tells you the minimum number of edits (insertions, deletions, substitutions) needed to transform one string into another. It's like calculating the steps to turn "kitten" into "sitting."

🔍 **Get Detailed Insight:**
Learn more about edit distance [**here**](https://towardsdatascience.com/how-to-improve-the-performance-of-a-machine-learning-model-with-post-processing-employing-b8559d2d670a).

---

### 📅 DAY 3: Diving into Text Representation Techniques

#### a. **Bag of Words (BoW): The Basic Building Block**
Imagine reducing a sentence to a simple bag of words, ignoring grammar and order. BoW does just that, focusing on the presence of words. It's a straightforward way to tokenize text but doesn't capture context.

🔍 **Understand BoW:**
Explore the concept of Bag of Words [**here**](https://ayselaydin.medium.com/4-bag-of-words-model-in-nlp-434cb38cdd1b).

#### b. **TF-IDF: Adding Depth to Word Representation**
TF-IDF adds a twist to BoW by weighing terms based on their importance. It highlights significant words while downplaying common ones. TF (Term Frequency) measures how often a word appears in a document, while IDF (Inverse Document Frequency) gauges the word's rarity across documents.

📊 **Formula Breakdown:**

- **Term Frequency (TF):**
$$
\text{TF}(t, d) = \frac{\text{Number of times term } t \text{ appears in document } d}{\text{Total number of terms in document } d}
$$

- **Inverse Document Frequency (IDF):**
$$
\text{IDF}(t, D) = \log \left( \frac{\text{Total number of documents}}{\text{Number of documents containing the term } t} \right)
$$

- **TF-IDF Score:**
$$
\text{TF-IDF}(t, d, D) = \text{TF}(t, d) \times \text{IDF}(t, D)
$$

🔍 **Explore More:**
Get a detailed understanding of TF-IDF [**here**](https://www.learndatasci.com/glossary/tf-idf-term-frequency-inverse-document-frequency/#:~:text=Term%20Frequency%20%2D%20Inverse%20Document%20Frequency%20(TF%2DIDF)%20is,%2C%20relative%20to%20a%20corpus).

#### c. **Continuous Bag of Words (CBOW): Contextual Word Embeddings**
CBOW is part of the Word2Vec family, predicting a target word based on its context. It's like filling in the blanks in a sentence using surrounding words, capturing semantic relationships.

🧠 **How It Works:**
For the sentence "The quick brown fox jumps over the lazy dog" and the target word "fox," CBOW uses the context ("The," "quick," "jumps," "over") to predict "fox."

🔍 **Discover CBOW:**
Learn more about Continuous Bag of Words [**here**](https://www.geeksforgeeks.org/continuous-bag-of-words-cbow-in-nlp/).

---

### 📅 DAY 4: [Diving into One-Hot Encoding and Word Embeddings](https://medium.com/intelligentmachines/word-embedding-and-one-hot-encoding-ad17b4bbe111)

#### a. [One-Hot Encoding](https://medium.com/analytics-vidhya/one-hot-encoding-of-text-data-in-natural-language-processing-2242fefb2148): The Basic Building Block of NLP

Imagine trying to categorize a group of objects where each object belongs to a unique category. One-hot encoding is like assigning a unique ID card to each word in your text, where each card contains just one slot marked "1" and all other slots marked "0." This approach transforms words into a format that machines can understand and process.

- **How It Works:** Each word is represented as a vector with a length equal to the total number of unique words (vocabulary size). In this vector, only one element is "hot" (set to 1), and the rest are "cold" (set to 0).
- **Example:** For a vocabulary of ['apple', 'banana', 'cherry']:
- 'apple' → [1, 0, 0]
- 'banana' → [0, 1, 0]
- 'cherry' → [0, 0, 1]

🔍 **Explore More:**
Learn more about one-hot encoding [here](https://www.geeksforgeeks.org/ml-one-hot-encoding/).

#### b. [Word Embeddings](https://colah.github.io/posts/2014-07-NLP-RNNs-Representations/): Adding Depth to Word Representations

#### *You shall know a word by the company it keeps — J.R. Firth*

While one-hot encoding is simple, it doesn't capture the relationships or meanings of words. Enter word embeddings – a more sophisticated approach where words are represented as dense vectors in a continuous vector space. These vectors are learned from the text data itself, capturing semantic relationships and similarities between words.

- **How It Works:** Word embeddings are created using algorithms like Word2Vec, GloVe, or FastText. Each word is mapped to a vector of fixed size (e.g., 100 dimensions) where similar words have similar vectors.
- **Example:** For the words 'king', 'queen', 'man', and 'woman', word embeddings might capture the relationships like:
- 'king' - 'man' + 'woman' ≈ 'queen'
- 'man' and 'woman' will be closer to each other in the vector space than 'man' and 'banana'.

🔍 **Explore More:**
Dive deeper into word embeddings [here](https://towardsdatascience.com/understanding-nlp-word-embeddings-text-vectorization-1a23744f7223).

---
### 📅 **DAY 5: Unlocking the Power of Pretrained Embeddings**

#### a. **Pretrained Embeddings**: Supercharging NLP with Pre-trained Knowledge

Think of pretrained embeddings as getting a head start in a race. Instead of starting from scratch, you leverage the knowledge already learned from massive text corpora. This can significantly boost your NLP models' performance by providing rich, contextual word representations right out of the box.

- **How It Works:** Pretrained embeddings, such as those from Word2Vec, GloVe, and FastText, are trained on large datasets like Wikipedia or Common Crawl. These embeddings capture nuanced word meanings and relationships, offering a robust foundation for various NLP tasks.
- **Benefits:**
- **Efficiency:** Saves time and computational resources since the heavy lifting of training embeddings has already been done.
- **Performance:** Often leads to better model performance due to the high-quality, contextual word representations.
- **Transfer Learning:** Facilitates transfer learning, where knowledge from one task (like language modeling) can be applied to another (like sentiment analysis).

#### b. Popular Pretrained Embeddings and How to Use Them

#### [Word2Vec](https://www.youtube.com/watch?v=UqRCEmrv1gQ&t=5s)
- For a really good explantion you can watch the [working of word2vec](https://www.youtube.com/watch?v=8rXD5-xhemo)
- **Description:** Word2Vec models come in two flavors – Continuous Bag of Words (CBOW) and Skip-gram. Both capture word relationships effectively.
- **How to Use:** Available via libraries like Gensim. Simply load the pretrained model and start using the embeddings in your projects.

#### [GloVe](https://towardsdatascience.com/glove-research-paper-explained-4f5b78b68f89) (Global Vectors for Word Representation)
- **Description:** GloVe embeddings are generated by aggregating global word-word co-occurrence statistics from a corpus.It can help in dealing with languages for which the word2vec and glove fail as they are trained mainly for english.
- **How to Use:** Pretrained GloVe vectors can be downloaded and integrated into your models using libraries like Gensim or directly via NumPy.

#### [FastText](https://fasttext.cc/)
- **Description:** Unlike Word2Vec and GloVe, FastText considers subword information, making it effective for morphologically rich languages and rare words.
- **How to Use:** Available via the FastText library. Load pretrained vectors and incorporate them into your models with ease.

🔍 **Explore More:**
Dive deeper into pretrained embeddings and their applications [here](https://patil-aakanksha.medium.com/top-5-pre-trained-word-embeddings-20de114bc26).

---
### 📅 **DAY 6: Understanding Sentiment Classification**
This is the first real world use case of NLP that we are going to discuss from scratch.
#### a. Sentiment Classification: Uncovering Emotions in Text

Imagine trying to understand someone's mood just by reading their messages. Sentiment classification does exactly that – it helps in identifying the emotional tone behind a body of text, whether it's positive, negative, or neutral. This technique is widely used in applications like customer feedback analysis, social media monitoring, and more.
By Definition, Sentiment analysis is a process that involves analyzing textual data such as social media posts, product reviews, customer feedback, news articles, or any other form of text to classify the sentiment expressed in the text.
- **How It Works:** Sentiment classification models analyze text and predict the sentiment based on the words and phrases used.The sentiment can be classified into three categories: Positive Sentiment Expressions indicate a favorable opinion or satisfaction; Negative Sentiment Expressions indicate dissatisfaction, criticism, or negative views; and Neutral Sentiment Text expresses no particular sentiment or is unclear.
These models can be built using various algorithms, from simple rule-based approaches to complex machine learning techniques.
- **Example:** For the sentence "I love this product!":
- The model would classify it as positive.
- For "I hate waiting for customer service," it would classify it as negative.

🔍 **Explore More:**
Learn more about sentiment classification [here](#).

### b. Techniques for Sentiment Classification

#### Rule-Based Methods
- **Description:** These methods use a set of manually created rules to determine sentiment. For example, lists of positive and negative words can be used to score the sentiment of a text.
- **Pros:** Simple and interpretable.
- **Cons:** Limited by the quality and comprehensiveness of the rules.

#### Machine Learning Methods
- **Description:** These methods use labeled data to train classifiers like Naive Bayes, SVM, or logistic regression. The models learn from the data and can generalize to new, unseen texts.
- **Pros:** More flexible and accurate than rule-based methods.
- **Cons:** Require labeled data for training and can be computationally intensive.

#### Deep Learning Methods
- **Description:** These methods leverage neural networks, such as RNNs, LSTMs, or transformers, to capture complex patterns in the text. Pretrained models like BERT and GPT can also be fine-tuned for sentiment analysis.
- **Pros:** State-of-the-art performance, capable of capturing nuanced sentiments.
- **Cons:** Require significant computational resources and large amounts of data.

#### [Here](https://medium.com/@robdelacruz/sentiment-analysis-using-natural-language-processing-nlp-3c12b77a73ec) is the link of a really good article to learn the techniques of sentiment classification and write code for it .

### 🎥 Watch and Learn: Sentiment Analysis in Action

For a detailed walkthrough on sentiment analysis using NLP techniques, check out this comprehensive video tutorial:

[](https://youtu.be/4YGkfAd2iXM?si=0mT-IxkrTvS7Ughz)

In this video, you’ll learn about:
- The basics of sentiment analysis
- Preprocessing steps for textual data
- Techniques for building sentiment analysis models
- Evaluating model performance

thus it will help you revise the whole week of content.....

---
### 📅 DAY 7: Hands-On Projects to Kickstart Your NLP Journey

Congratulations on making it through the first week of your NLP journey! Today, we're going to dive into some beginner-friendly projects to help you apply what you've learned. These projects will solidify your understanding and give you practical experience in working with NLP.

#### 🔧 Project Ideas for Beginners

1. **Sentiment Analysis on Movie Reviews**
- **Objective:** Build a model to classify movie reviews as positive or negative.
- **Dataset:** [IMDb Movie Reviews](https://ai.stanford.edu/~amaas/data/sentiment/)
- **Tools:** Python, NLTK, Scikit-learn, Pandas
- **Steps:**
1. Preprocess the text data (tokenization, removing stop words, etc.).
2. Convert text to numerical features using TF-IDF.
3. Train a machine learning model (e.g., logistic regression).
4. Evaluate the model's performance.

2. **Text Classification for News Articles**
- **Objective:** Categorize news articles into different topics (e.g., sports, politics, technology).
- **Dataset:** [20 Newsgroups Dataset](http://qwone.com/~jason/20Newsgroups/)
- **Tools:** Python, Scikit-learn, Pandas
- **Steps:**
1. Preprocess the text data.
2. Convert text to numerical features using count vectorization.
3. Train a classification model (e.g., Naive Bayes).
4. Evaluate the model's accuracy and fine-tune it.

3. **Spam Detection in Emails**
- **Objective:** Create a model to identify spam emails.
- **Dataset:** [SpamAssassin Public Corpus](http://spamassassin.apache.org/publiccorpus/)
- **Tools:** Python, NLTK, Scikit-learn, Pandas
- **Steps:**
1. Preprocess the text data.
2. Feature extraction using count vectorization or TF-IDF.
3. Train a machine learning model (e.g., SVM).
4. Test the model and improve its performance.

4. **Named Entity Recognition (NER)**
- **Objective:** Identify and classify named entities (like people, organizations, locations) in text.
- **Dataset:** [CoNLL-2003 NER Dataset](https://www.clips.uantwerpen.be/conll2003/ner/)
- **Tools:** Python, SpaCy
- **Steps:**
1. Preprocess the text data.
2. Use SpaCy to build and train a NER model.
3. Evaluate the model's performance on test data.

### 🚀 Try It Yourself!

Pick one or more of these projects and get started. Don't worry if you face challenges along the way; it's all part of the learning process. As you work on these projects, you'll gain a deeper understanding of NLP techniques and improve your coding skills.

Remember, practice makes perfect. The more you experiment with different datasets and models, the more proficient you'll become in NLP. Happy coding!