2025 Natural Language Processing (NLP) Mastery in Python

This comprehensive course will teach you Natural Language Processing (NLP) from scratch, leveraging Python for beginners. With over 38 hours of engaging content, this course is a hands-on learning journey that covers fundamental techniques and tools to process text data and deploy machine learning models. By the end of the course, you'll gain valuable skills to implement text processing, machine learning, deep learning, and text classification models.

Introduction:

Start your journey with a gentle introduction to machine learning principles. You'll get a clear overview of this exciting field before jumping into installing all necessary software like Anaconda, Python, VS Code, and Git Bash. With step-by-step instructions for different operating systems (Windows, Ubuntu, and Mac), you'll be equipped to run Python code seamlessly using Jupyter Notebooks.

Python Crash Course for Machine Learning:

Build a solid foundation in Python, specifically tailored for machine learning. Learn Python data types, control flow, loops, functions, and error handling. You'll master using lists, dictionaries, sets, and tuples effectively, enabling you to write clean, efficient code in no time.

Numpy Crash Course for Machine Learning:

Gain proficiency in Numpy, the essential library for numerical computing in Python. Learn how to create, manipulate, and perform statistical operations on arrays. You’ll also understand how to work with multidimensional arrays, reshaping them, and performing advanced operations like sorting and handling NaN values, key to working with datasets in ML.

Pandas Crash Course for Machine Learning:

In this section, you’ll dive into Pandas, a critical tool for data manipulation and analysis. Learn how to load, filter, slice, and clean your data using advanced techniques like Groupby, Aggregation, and merging. You'll also focus on handling missing data and effectively preparing data for ML algorithms.

Working with Text Files:

Understand how to handle a variety of file formats, from basic text files to CSV, Excel, and JSON files. You’ll explore how to write, read, and process these files to extract and prepare the information for Machine Learning tasks. Special focus will be given to cleaning and extracting data from complex files like PDFs and audio files.

Mastering Regular Expressions with Python:

Learn the power of Regular Expressions (Regex) to clean and preprocess text data efficiently. This section covers pattern matching, extracting relevant information, and working with text data using regex functions in Python.

Spacy Introduction for Text Processing:

Discover Spacy, an industry-standard library for text processing and NLP. You’ll learn how to tokenize, tag parts of speech (POS), and extract named entities like person names and locations using Spacy’s pre-built models. These tools will be crucial in processing large amounts of text data.

NLTK for Text Processing:

Explore the Natural Language Toolkit (NLTK) for text processing. Learn tokenization, stemming, and lemmatization. You'll also get hands-on with Named Entity Recognition (NER), chunking, and identifying collocations in text data.

Complete Text Cleaning and Text Processing:

Go deep into text cleaning with a full overview of common cleaning tasks, such as removing URLs, mentions, hashtags, and stopwords, as well as expanding contractions. You'll also be introduced to advanced tasks like spelling correction, word cloud visualizations, and sentiment analysis using the TextBlob library.

Make Your Own Text Processing Python Package:

This section empowers you to build your own Python package. After setting up your project directory and necessary files, you'll implement methods to encapsulate your text processing workflows. Learn the significance of tools like setup[dot]py for package distribution.

Publish Your Python Package on PyPi for Easy Installation:

Learn the process of publishing your text processing package on PyPi, making it easy for others to install via pip. This section walks you through creating GitHub repositories, uploading your work, and sharing your package for open-source usage.

Linear Regression and Interview Questions:

Gain insights into one of the foundational machine learning algorithms—Linear Regression. Learn how to code it for tasks like predicting housing prices and using evaluation metrics like Mean Squared Error (MSE). You’ll also explore common interview questions on regression models.

Logistic Regression and Interview Questions:

Delve into Logistic Regression, understanding how it works for binary classification tasks like predicting whether a tumor is malignant or benign. Get ready to answer key questions about cost functions, entropy, and overfitting.

SVM, KNN, Decision Tree, Random Forest and Interview Questions:

In this section, understand some of the most common machine learning classifiers, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees. You will train models and fine-tune them for optimal performance.

Spam Text Classification:

Learn how to build a spam email classifier using classic techniques like Bag of Words (BoW) and TF-IDF. You'll explore the process from feature extraction, data loading, model training, and evaluation.

Sentiment Analysis on IMDB Movie Reviews:

Explore sentiment analysis by predicting movie reviews from IMDB. You’ll use TF-IDF and various machine learning models like Logistic Regression and SVM for analysis, gaining crucial insights into working with text sentiment classification tasks.

ML Model Deployment with Flask:

Learn how to deploy machine learning models as a web application using Flask. This section covers setting up a Flask server, running your ML models on it, and deploying your machine learning API for real-time prediction.

Multi-Label Text Classification for Tag Prediction:

Master multi-label classification, a technique where each instance can belong to more than one label. You'll apply it to the Stack Overflow dataset, focusing on predicting multiple tags for a post.

Sentiment Analysis using Word2Vec Embeddings:

Dive deeper into word embeddings like Word2Vec and GloVe to enhance your sentiment analysis models. By training machine learning algorithms using these word vectors, you'll increase the performance and accuracy of your models.

Resume Parsing with Spacy:

Learn to implement Named Entity Recognition (NER) using Spacy for parsing Resumes (CVs). This powerful skill can automate tasks such as extracting key information from resumes, which is highly applicable in talent acquisition or HR automation.

Deep Learning for Sentiment Analysis:

Explore Deep Learning techniques for text sentiment analysis, including building and training an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN). Understand why deep learning models are so effective in working with complex text data.

Hate Speech Classification using Deep Learning:

Focus on Deep Learning for classifying text, especially for applications like hate speech detection. By building a model using CNN, you will classify tweets and gain understanding of building powerful models for text categorization.

Poetry Generation Using LSTM and TensorFlow/Keras:

Explore how to generate text automatically with Long Short-Term Memory (LSTM) networks using TensorFlow and Keras. By training your models on poetry datasets, you’ll understand how to create creative applications in the field of text generation.

Disaster Tweets Classification Using Deep Learning:

Learn how to classify Disaster Tweets with deep learning and embeddings. This project helps you see how sentiment analysis can be scaled to real-world scenarios with a focus on disaster management communication analysis.

Each section of this course will enrich your knowledge and prepare you for hands-on tasks in Natural Language Processing and Machine Learning, creating opportunities to master real-world projects and prepare for job-ready NLP tasks.

Note:

This course requires you to download Anaconda and/or Docker Desktop from external websites. If you are a Udemy Business user, please check with your employer before downloading software.