Course Highlights
- From spam filters to ChatGPT, computer and AI systems have to work with language models all the time.
- In this course, you will learn how to build and work with the most common language models in Data Science, including bag-of-words (BoW), TF-IDF, and word embeddings.
- Learn the basic skills you need before working with more advanced AI language models.
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Skill Type
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Course Duration
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Domain
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GOI Incentive applicable
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Course Category
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Nasscom Assessment
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Placement Assistance
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Certificate Earned
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Content Alignment Type
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NOS Details
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Mode of Delivery
Course Details
What will you learn in the Learn Python Course?
- Understand the Role of Language Models:
Explain what language models are and how they are used in Natural Language Processing (NLP) and real-world AI applications. - Preprocess and Clean Text Data:
Apply fundamental text preprocessing techniques such as tokenization, stop-word removal, stemming, and lemmatization using Python. - Implement Core Language Models:
Build and use basic NLP models including -- Bag-of-Words (BoW)
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word Embeddings (e.g., Word2Vec or GloVe)
- Compare and evaluate models
- Apply models to real-world scenarios
- Develop foundational NLP skills in Python
- Prepare for advanced NLP and AI courses
Why should you take the Learn Python Course?
- Foundation for NLP and AI
- Hands-on Python practice
- Career-boosting skills
- Bridge to advanced topics
- No prior NLP experience required
- Real-world applications
- Learn at your pace
Who should take the Learn Python Course?
- Aspiring Data Scientists
- Python Programmers
- Machine Learning Enthusiasts
- Students and fresh Graduates
- Working Professionals Upskilling in AI
- Product Managers and Tech Leads
- Researchers and Academics
Curriculum
1. Welcome to Language Models with Python
A brief overview of what you will learn in this course.
- Informational - Welcome to Language Models with Python
2. Bag-of-Words Language Model
When your language model appetite with the widely used Bag-of-Words. Develop the underlying functionality in Python, then use scikit-learn.
- Lesson - Bag-of-Words Language Model
- External Resource - Working with Text Data | scikit-learn
- Project - Mystery Friend
- Quiz - Bag-of-Words Language Model
3. Term Frequency-Inverse Document Frequency (TF-IDF)
Rethink topic models with Term Frequency-Inverse Document Frequency (TF-IDF), which adjusts the importance of words within a document.
- Lesson - Term Frequency-Inverse Document Frequency
- External Resource - Working with Text Data | scikit-learn | From Occurrences to Frequencies
- Project - Read the News Analysis
- Quiz - Term Frequency-Inverse Document Frequency
4. Word Embeddings
Quantify meaning based on context using word embeddings.
- Lesson - Word Embeddings
- External Resource - Token Similarity | spaCy
- Project - USA Presidential Vocabulary
- Quiz - Word Embeddings
- Informational - What's Next
Tools you will learn in the Learn Python Course
- Learn UI/UX theory and practice
- Understand common methodologies
- Practice new skills with Figma