Course Details
Course Name: NLP in Python: Probability Models, Statistics, Text Analysis
Students: 15,628+
Avg Rating: 4.3 (47 ratings)
Duration: 6.5 hours on-demand video
Original Price: ₹799 / ~$14.99
Current Price: ₹399 / ~$4.99 (Price may vary by country)
Discount: 50% OFF
Course Type: Lifetime Access
Refund: 30-Day Money-Back Guarantee
Included: Mobile/TV access, downloadable certificate
What You’ll Learn
- Build a complete sentiment analysis pipeline using rule-based + machine learning approaches
- Master text preprocessing methods: tokenization, cleaning, stopwords, stemming, lemmatization
- Work with feature extraction techniques: TF-IDF, word embeddings, bag-of-words
- Build custom text classification systems from scratch
- Create Named Entity Recognition (NER) systems using probability models
- Integrate NER with modern NLP libraries like spaCy
- Train language models using Bayesian methods, Naive Bayes, and Bayesian Networks
- Understand & implement N-grams, Hidden Markov Models (HMMs), PCFGs
- Build a full e-commerce review analysis system (sentiment + NER + topic modeling)
- Learn probability-based Natural Language Processing through hands-on projects
Why Learn This Course?
- NLP is one of the fastest-growing fields in AI, powering chatbots, search engines, recommendation systems, and text analytics
- This course focuses on probability models—a core foundation behind modern and classical NLP
- You learn practical, job-ready skills with real Python examples
- Ideal for data scientists, ML engineers, AI beginners, and developers wanting to understand language models
- Useful for students preparing for roles in AI, analytics, and research
- Short, crisp 6.5-hour runtime makes it easy to complete while still learning advanced concepts
- Hands-on projects help you build a strong GitHub portfolio
FAQs
1. Is this course good for beginners?
Yes, beginners with basic Python knowledge can follow comfortably.
2. Does it include real applications?
Yes — you build a complete e-commerce review analysis system.
3. Will I learn probability-based NLP?
Absolutely. It covers N-grams, HMMs, Bayesian methods, and PCFGs.
4. Does the course teach deep learning?
No — it focuses on classical probability models, not deep neural networks.
5. Will this help in AI/ML jobs?
Yes. Understanding classical NLP gives you a strong base for advanced NLP roles.
Final Note
Affiliate Disclaimer: Some links in this post may be affiliate links. This means we may earn a small commission at no extra cost to you. These commissions help support the site — thank you for your support!
This course is perfect if you want to master the logic behind NLP rather than just using pre-built deep learning tools. It teaches probability-driven models, sentiment pipelines, NER, and text classification in a simple, practical way. Ideal for beginners, students, and professionals wanting to advance in NLP, analytics, and AI — especially at the current discounted price.
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