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

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.

Who this course is for:

• 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 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

More Info