In Data Science: Natural Language Processing (NLP) in Python course, students will learn to build practical systems using Natural Language Processing (NLP). NLP is the branch of Machine Learning and Data Science that deals with text and speech. Students should know basic Python and how to install numerical libraries for Python.
The course is designed with hands-on activities that are based on real-world problems. Currently, Udemy is offering the Data Science: Natural Language Processing (NLP) in Python Course for up to 87 % off i.e. INR 449 (INR 3,499).
Who all can opt for this course?
- Students who are comfortable writing Python code, and using loops, lists, dictionaries, etc.
- Students who don’t want to study a lot of math but want to learn more about Machine Learning.
- Professionals with an interest in using NLP and Machine Learning to solve real-world issues including sentiment analysis, spam detection, and internet marketing.
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 11 hours |
| Rating | 4.6/5 |
| Student Enrollment | 43,199 students |
| Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |
| Topics Covered | Building a Cipher Decryption Algorithm, Sentiment Analysis in Python, Markov Models, NLTK Exploration |
| Course Level | Intermediate |
| Total Student Reviews | 11,270 |
Learning Outcomes
- Create your own cipher decryption algorithm utilizing Markov language modeling and evolutionary algorithms
- Create your own Python spam detection code
- Create your own Python sentiment analysis code
- Use Python to carry out latent semantic analysis or latent semantic indexing
- Have a plan for creating your own Python article spinner
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Natural Language Processing – What is it used for? (21 minutes) | Introduction and Outline |
| Why Learn NLP? | ||
| The Central Message of this Course (Big Picture Perspective) | ||
| 2. | Course Preparation (23 minutes) | How to Succeed in this Course |
| Where to get the code and data | ||
| How to Open Files for Windows Users | ||
| 3. | Machine Learning Basics Review (01 hour 42 minutes) | Machine Learning: Section Introduction |
| What is Classification? | ||
| Classification in Code | ||
| What is Regression? | ||
| Regression in Code | ||
| What is a Feature Vector? | ||
| Machine Learning is Nothing but Geometry | ||
| All Data is the Same | ||
| Comparing Different Machine Learning Models | ||
| Machine Learning and Deep Learning: Future Topics | ||
| Section Summary | ||
| 4. | Markov Models (01 hour 47 minutes) | Markov Models Section Introduction |
| The Markov Property | ||
| The Markov Model | ||
| Probability Smoothing and Log-Probabilities | ||
| Building a Text Classifier (Theory) | ||
| Building a Text Classifier (Exercise Prompt) | ||
| Building a Text Classifier (Code pt 1) | ||
| Building a Text Classifier (Code pt 2) | ||
| Language Model (Theory) | ||
| Language Model (Exercise Prompt) | ||
| Language Model (Code pt 1) | ||
| Language Model (Code pt 2) | ||
| Markov Models Section Summary | ||
| 5. | Decrypting Ciphers (01 hour 31 minutes) | Section Introduction |
| Ciphers | ||
| Language Models | ||
| Genetic Algorithms | ||
| Code Preparation | ||
| Code pt 1 | ||
| Code pt 2 | ||
| Code pt 3 | ||
| Code pt 4 | ||
| Code pt 5 | ||
| Code pt 6 | ||
| Section Conclusion | ||
| 6. | Build your own spam detector (01 hour 04 minutes) | Build your own spam detector – description of data |
| Build your own spam detector using Naive Bayes and AdaBoost – the code | ||
| Key Takeaway from Spam Detection Exercise | ||
| Naive Bayes Concepts | ||
| AdaBoost Concepts | ||
| Other types of features | ||
| Spam Detection FAQ (Remedial #1) | ||
| What is a Vector? (Remedial #2) | ||
| SMS Spam Example | ||
| SMS Spam in Code | ||
| Suggestion Box | ||
| 7. | Build your own sentiment analyzer (01 hour 00 minutes) | Description of Sentiment Analyzer |
| Logistic Regression Review | ||
| Preprocessing: Tokenization | ||
| Preprocessing: Tokens to Vectors | ||
| Sentiment Analysis in Python using Logistic Regression | ||
| Sentiment Analysis Extension | ||
| How to Improve Sentiment Analysis & FAQ | ||
| 8. | NLTK Exploration (09 minutes) | NLTK Exploration: POS Tagging |
| NLTK Exploration: Stemming and Lemmatization | ||
| NLTK Exploration: Named Entity Recognition | ||
| Want more NLTK? | ||
| 9. | Latent Semantic Analysis (44 minutes) | Latent Semantic Analysis – What does it do? |
| SVD – The underlying math behind LSA | ||
| Latent Semantic Analysis in Python | ||
| What is Latent Semantic Analysis Used For? | ||
| Extending LSA | ||
| 10. | Write your own article spinner (37 minutes) | Article Spinning Introduction and Markov Models |
| Trigram Model | ||
| More about Language Models | ||
| Precode Exercises | ||
| Writing an article spinner in Python | ||
| Article Spinner Extension Exercises | ||
| 11. | How to learn more about NLP (02 minutes) | What we didn’t talk about |
| 12. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |
| How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||
| 13. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (42 minutes) | How to Code by Yourself (part 1) |
| How to Code by Yourself (part 2) | ||
| Proof that using Jupyter Notebook is the same as not using it | ||
| Python 2 vs Python 3 | ||
| 14. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (59 minutes) | How to Succeed in this Course (Long Version) |
| Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? | ||
| Machine Learning and AI Prerequisite Roadmap (pt 1) | ||
| Machine Learning and AI Prerequisite Roadmap (pt 2) | ||
| 15. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |
| BONUS |
Resources Required
- Install Python for free
- Writing Python code should be at least somewhat natural to you
- Understand how to install Python’s numerical libraries, including Matplotlib, Scipy, Scikit-Learn, and BeautifulSoup
- Optional: Probability and linear algebra are useful for helping you comprehend the mathematical components
Featured Review
Karthik Balakrishnan (5/5): Great introductory course to NLP! Despite having a background in Machine Learning besides being a developer, NLP is something I had never delved into. This course albeit short, it a good introduction to concepts in this space. As the instructor says, intuition is developed in hindsight, and not getting your hands dirty leads to weak fundamentals. I’m on my way to the “advanced” course with Deep Learning next. Just one suggestion – watch the Appendix lectures before starting on this course. The instructor provides several vital tips!
Pros
- Patrick Olson (4/5): This is an excellent course to get me started on NLP.
- Andrei Yevseyev (5/5): The best part is that I implemented an end to end system of NLP tasks
- Adnan Ali (5/5): This is the best way to learn applications and basics of NLP.
- Sowjanya (5/5): Explanation is very good and the design / order of content is excellent
Cons
- Marina Pliusnina (2/5): The worst course I ve taken in the last 2 years.
- Marina Pliusnina (2/5): No logic in the structure of the course and in the material itself, jumping from difficult topics to first-to-learn ones.
- Eugen Klein (2/5): The author decided not to dwell on theory behind the ML algorithms, but instead to concentrate on NLP/ NLP applications.
- Eugen Klein (2/5): Explanations are superficial and a lot of times incorrect or just plain useless.
About the Author
The instructor of this course is Lazy Programmer Inc. He works as an Artificial Intelligence and Machine Learning Engineer. With a 4.6 instructor rating and 1,41,144 reviews on Udemy, he offers 32 courses and has taught 5,12,747 students so far.
- Although he is recognized as a data scientist, big data engineer, and full stack software engineer, he currently spends the majority of his time as an artificial intelligence and machine learning engineer with an emphasis on deep learning.
- He earned his master’s degree in computer engineering with a focus on machine learning and pattern recognition more than ten years ago.
- His second master’s degree was in statistics with a focus on financial engineering.
- He is a data scientist and big data engineer with experience in online advertising and digital media (optimizing click and conversion rates, and building data processing pipelines).
- He routinely uses big data technologies like Hadoop, Pig, Hive, MapReduce, and Spark.
- He has developed deep learning models for text modeling, image and signal processing, user behavior prediction, and click-through rate estimation.
- He has also taught students at universities like Columbia University, NYU, Hunter College, etc.
Comparison Table
| Parameters | Data Science: Natural Language Processing (NLP) in Python | Unsupervised Machine Learning Hidden Markov Models in Python | Natural Language Processing with Deep Learning in Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 12 hours | 10 hours | 12 hours |
| Rating | 4.6 /5 | 4.6 /5 | 4.6 /5 |
| Student Enrollments | 43,199 | 25,050 | 42,420 |
| Instructors | Lazy Programmer Inc. | Lazy Programmer Team | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Data Science: Natural Language Processing (NLP) in Python: FAQs
Ques. Why is NLP needed?
Ans. NLP is required to scale language-related tasks; natural language processing enables computers to converse with people in their own language. For instance, NLP enables computers to read text, hear a voice, analyze it, gauge sentiment, and identify the key points.
Ques. Can Python be used for NLP?
Ans. Making natural human language accessible to computer programs is the goal of the field of natural language processing (NLP). You can use the Python library NLTK, or Natural Language Toolkit, for NLP. A large portion of the data that you might be examining is unstructured and contains text that can be read by humans.
Ques. What are the applications of NLP?
Ans. NLP is used in a variety of tasks such as Email filters, smart assistants, predictive texts, search results, text analytics, etc.
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