Introduction to Natural Language Processing in Python [2024]

pandas, numpy, seaborn, matplotlib, spaCy, Stop-word removal, Case folding, XGBOOST, TextBlob, Hierarchical Clustering
What you'll learn:
pandas
numpy
seaborn
matplotlib
spaCy
lemmatization
tokenization
Stop-word removal
Case folding
N-grams
XGBOOST
Word2vec
skip-gram
Bag of words
Zipf's law
TF-IDF
Feature engineering
WordCloud
Hierarchical Clustering
Sampling
Removing Correlated features
Dimensionality reduction
Tree methods
TextBlob
keras
Requirements:
Basic knowledge of Python programming
Familiarity with data manipulation and analysis using Python libraries such as NumPy and pandas
No prior experience in NLP is required, but a strong interest in language processing and machine learning is recommended.
Description:
Natural Language Processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence. This course provides a comprehensive introduction to NLP using the Python programming language, covering fundamental concepts, techniques, and tools for analyzing and processing human language data.Throughout the course, students will learn how to leverage Python libraries such as NLTK (Natural Language Toolkit), spaCy, and scikit-learn to perform various NLP tasks, including tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, text classification, and language modeling.The course begins with an overview of basic NLP concepts and techniques, including text preprocessing, feature extraction, and vectorization. Students will learn how to clean and preprocess text data, convert text into numerical representations suitable for machine learning models, and visualize textual data using techniques such as word clouds and frequency distributions.Next, the course covers more advanced topics in NLP, including syntactic and semantic analysis, grammar parsing, and word embeddings. Students will explore techniques for analyzing the structure and meaning of sentences and documents, including dependency parsing, constituency parsing, and semantic role labeling.The course also introduces students to practical applications of NLP in various domains, such as information retrieval, question answering, machine translation, and chatbot development. Students will learn how to build and evaluate NLP models using real-world datasets and evaluate their performance using appropriate metrics and techniques.By the end of the course, students will have a solid understanding of the fundamental principles and techniques of NLP and the ability to apply them to solve real-world problems using Python. Whether you are a beginner or an experienced Python programmer, this course will provide you with the knowledge and skills you need to start working with natural language data and build intelligent NLP applications.Course Outline:Introduction Course strucureHow to make out of this courseOverview of natural language processingText pre-processingTokenization techniques (word-level, sentence-level) and its implementationRegular expression and its implementationTreebank tokenizer and its implementationTweetTokenizer and its implementationStemming and its implementationWordNet Lemmatizer and its implementationspacy Lemmatizer and its implementationThe introduction and implementation of stop word removalThe introduction and implementation of Case foldingIntroduction and implementation of N-gramsText RepresentationIntroduction to Word2vec and implementationskip-gram implementationBag of word implementationHow to perform basic feature extraction methodsWhat are types of dataText cleaning and tokenization practice.How to perform text tokenization using keras and TextBlobSingularizing and pluralizing words and language translationWhat does feature extraction mean in natural language processingImplementation of feature extraction in natural language processing.Introduction to Zipf's Law and implementationIntroduction to TF-IDF and implementationfeature engineeringIntroduction to WordCloud and its implementationspaCy overview and implementationIntroduction to spaCyTokenization Implementationlemmatization ImplementationText Classifier ImplementationIntroduction to Machine learningIntroduction to Hierarchical Clustering and implementationintroduction to K-means Clustering and implementationIntroduction to Text Classification and implementation introduction to tree methods and implementationintroduction to Removing Correlated Features and implementationintroduction to Dimensionality Reduction and implementationMode of Instruction:The course will be delivered through a combination of lectures, demonstrations, hands-on exercises, and project work.Students will have access to online resources, including lecture slides, code examples, and additional reading materials.Instructor-led sessions will be supplemented with self-paced learning modules and group discussions.certification:Upon successful completion of the course, students will receive a certificate of completion, indicating their proficiency in natural language processing with Python.Join us on a journey into the fascinating world of natural language processing and discover the endless possibilities for building intelligent applications that can understand and interact with human language data. Enroll now and take the first step towards mastering the art of NLP with Python!
Who this course is for:
Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning
Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Data Scientists who want to take their AI Skills to the next level.
AI experts who want to expand on the field of applications.
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
Any people who are not satisfied with their job and who want to become a Data Scientist.
Software developers, data scientists, and researchers interested in natural language processing
Professionals seeking to expand their skill set and explore new career opportunities in NLP and related fields
Students and academics looking to learn about state-of-the-art techniques and tools in NLP and apply them to their research projects
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