Video Description
It starts with a course overview and gives students a special offer to get started on this course. The first section guides you through the setup process and provides the necessary code resources. The course focuses on strategies for success to ensure learners are well prepared from the start.
This course delves into spam detection, starting with the problem statement and the intuition behind Naive Bayes. Participants engage in exercises and learn how to address class imbalance while evaluating models using ROC, AUC, and F1 scores. Hands-on implementation in Python deepens understanding. The course then moves to sentiment analysis, covering the problem statement, logistic regression, and training. Exercises and Python-based projects allow learners to effectively apply the concepts.
Text summarization is thoroughly explored from beginner to advanced levels with sections on vector-based methods and TextRank. Hands-on Python sessions enable learners to implement these techniques. The course concludes with topic modeling, introducing LDA and NMF methods, complemented by Python coding exercises. A detailed look at Latent Semantic Analysis and the application of SVD in NLP rounds off the curriculum, providing you with comprehensive expertise in NLP.
What you will learn
- Identify and implement spam detection algorithms
- Using logistic regression for sentiment analysis
- Perform text summarization using various methods
- Apply advanced techniques such as TextRank to summarization
- Understand and implement topic modeling using LDA and NMF
- Leveraging Latent Semantic Analysis in Python Projects
audience
This course is designed for technical professionals, data scientists, and developers with a basic understanding of Python programming and machine learning concepts. Familiarity with basic statistical methods is recommended, but not required.
About the Author
Lazy programmers: Renowned online educator, The Lazy Programmer, has two Masters degrees in Computer Engineering and Statistics and has specialized in Machine Learning, Pattern Recognition and Deep Learning for a decade, writing pioneering courses. His professional background includes enhancing online advertising and digital media, significantly increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His breadth of knowledge covers areas such as bioinformatics and algorithmic trading, which speaks to his diverse skill set. Dedicated to simplifying complex topics, he is a central figure in online education, expertly guiding students through the nuances of Data Science and AI.
