Machine Learning: Natural Language Processing in Python (V2)

Embark on a transformative learning journey with our free video course, “Machine Learning: Natural Language Processing in Python (V2),” hosted on howtofree.org. Delve into the captivating realm of Natural Language Processing (NLP) through 30+ hours of immersive video content that demystifies the intricacies of Python for NLP.

Uncover the power of machine learning as you follow our comprehensive tutorial series. From fundamental concepts to advanced techniques, each video is crafted to foster a deep understanding of NLP in Python. Our engaging and educational tone ensures that learners of all levels can grasp the intricacies of this dynamic field.

This transformative journey covers a spectrum of topics, guiding you from fundamental concepts to advanced techniques in machine learning. Each video in our comprehensive tutorial series is meticulously crafted to facilitate a profound understanding of NLP in the Python programming language. Whether you’re a novice or an experienced learner, our engaging and educational tone ensures that participants of all levels can easily grasp the intricacies of this dynamic and fascinating field.

Don’t miss the chance to enrich your understanding, refine your skills, and master the art of Natural Language Processing. Join us on this empowering educational expedition and unlock the doors to a world where machine learning and Python converge seamlessly. Start your transformative learning journey today – knowledge awaits!

What You’ll Gain Expertise In:

• Text Vectorization Mastery:
• Learn the art of converting text into vectors using versatile techniques such as CountVectorizer, TF-IDF, word2vec, and GloVe.
• Building Powerful Search Engines:
• Implement a robust document retrieval system, search engine, similarity search, and vector similarity for efficient information retrieval.
• Foundation in Probability, Language, and Markov Models:
• Establish a strong foundation in probability models, language models, and Markov models—an essential prerequisite for advanced concepts like Transformers, BERT, and GPT-3.
• Cipher Decryption Algorithm Implementation:
• Dive into the intriguing world of cipher decryption algorithms using genetic algorithms and language modeling.
• Spam Detection Implementation:
• Acquire the skills to implement effective spam detection mechanisms, a crucial aspect of real-world NLP applications.
• Sentiment Analysis Expertise:
• Understand and implement sentiment analysis techniques to decipher the emotional tone behind textual content.
• Article Spinner Implementation:
• Explore the implementation of an article spinner, adding creativity and uniqueness to your text generation skills.
• Text Summarization Techniques:
• Master the art of text summarization, condensing information effectively for concise and impactful communication.
• Latent Semantic Indexing Proficiency:
• Gain expertise in implementing latent semantic indexing for advanced content understanding and organization.
• Topic Modeling Techniques:
• Implement topic modeling using LDA, NMF, and SVD, enhancing your ability to extract meaningful insights from textual data.
• Wide Array of Machine Learning Techniques:
• Delve into machine learning with techniques like Naive Bayes, Logistic Regression, PCA, SVD, and Latent Dirichlet Allocation.
• Deep Learning Fundamentals:
• Explore deep learning essentials, including ANNs, CNNs, RNNs, LSTM, and GRU—crucial prerequisites for understanding advanced models like BERT and GPT-3.
• Exclusive Insights into Hugging Face Transformers (VIP only):
• Access exclusive content on Hugging Face Transformers, gaining insights into cutting-edge developments in NLP.
• Tool Proficiency:
• Learn to leverage Python, Scikit-Learn, Tensorflow, and more for NLP applications, enhancing your programming prowess.
• Text Preprocessing Techniques:
• Master text preprocessing essentials, including tokenization, stopwords handling, lemmatization, and stemming for cleaner and more efficient text analysis.
• Advanced Language Understanding:
• Explore parts-of-speech (POS) tagging and named entity recognition (NER), enhancing your ability to dissect and understand the nuances of language.

Course Content

1. Introduction of Natural Language Processing in Python

• Introduction and Outline
• Are You Beginner, Intermediate, or Advanced? All are 0K!

2. Getting Set Up

• Get Your Hands Dirty, Practical Coding Experience, Data Links
• How to use Github & Extra Coding Tips (Optional)
• Where to get the code, notebooks, and data
• How to Succeed in This Course
• Temporary 403 Errors

3. Vector Models and Text Preprocessing

• Vector Models & Text Preprocessing Intro
• Basic Definitions for NLP
• What is a Vector?
• Bag of Words
• Count Vectorizer (Theory)
• Tokenization
• Stopwords
• Stemming and Lemmatization
• Stemming and Lemmatization Demo
• Count Vectorizer (Code)
• Vector Similarity
• TF-IDF (Theory)
• (Interactive) Recommender Exercise Prompt
• TF-IDF (Code)
• Word-to-Index Mapping
• How to Build TF-IDF From Scratch
• Neural Word Embeddings
• Neural Word Embeddings Demo
• Vector Models & Text Preprocessing Summary
• Text Summarization Preview
• How To Do NLP In Other Languages
• Suggestion Box

4. Probabilistic Models (Introduction)

• Probabilistic Models (Introduction)

5. Markov Models (Intermediate)

• 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

6. Article Spinner (Intermediate)

• Article Spinning – Problem Description
• Article Spinning – N-Gram Approach
• Article Spinner Exercise Prompt
• Article Spinner in Python (pt 1)
• Article Spinner in Python (pt 2)
• Case Study: Article Spinning Gone Wrong

7. Cipher Decryption (Advanced)

• Section Introduction
• Ciphers
• Language Models (Review)
• Genetic Algorithms
• Code Preparation
• Code pt 1
• Code pt 2
• Code pt 3
• Code pt 4
• Code pt 5
• Code pt 6
• Cipher Decryption – Additional Discussion
• Section Conclusion

8. Machine Learning Models (Introduction)

• Machine Learning Models (Introduction)

9. Spam Detection

• Spam Detection – Problem Description
• Naive Bayes Intuition
• Spam Detection – Exercise Prompt
• Aside: Class Imbalance, ROC, AIJC, and Fl Score (pt 1)
• Aside: Class Imbalance, ROC, AIJC, and Fl Score (pt 2)
• Spam Detection in Python

10. Sentiment Analysis

• Sentiment Analysis – Problem Description
• Logistic Regression Intuition (pt 1)
• Multiclass Logistic Regression (pt 2)
• Logistic Regression Training and Interpretation (pt 3)
• Sentiment Analysis – Exercise Prompt
• Sentiment Analysis in Python (pt 1)
• Sentiment Analysis in Python (pt 2)

11. Text Summarization

• Text Summarization Section Introduction
• Text Summarization Using Vectors
• Text Summarization Exercise Prompt
• Text Summarization in Python
• TextRank Intuition
• TextRank – How It Really Works (Advanced)
• TextRank Exercise Prompt (Advanced)
• TextRank in Python (Advanced)
• Text Summarization in Python – The Easy Way (Beginner)
• Text Summarization Section Summary

12. Topic Modeling

• Topic Modeling Section Introduction
• Latent Dirichlet Allocation (LDA) – Essentials
• LDA – Code Preparation
• LDA – Maybe Useful Picture (Optional)
• Latent Dirichlet Allocation (LDA) – Intuition (Advanced)
• Topic Modeling with Latent Dirichlet Allocation (LDA) in Python
• Non-Negative Matrix Factorization (NMF) Intuition
• Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python
• Topic Modeling Section Summary

13. Latent Semantic Analysis (Latent Semantic Indexing)

• LSA / LSI Section Introduction
• SVD (Singular Value Decomposition) Intuition
• LSA/ LSI: Applying SVD to NLP
• Latent Semantic Analysis / Latent Semantic Indexing in Python
• LSA / LSI Exercises

14. Deep Learning (Introduction)

• Deep Learning Introduction (Intermediate-Advanced)

15. The Neuron

• The Neuron – Section Introduction
• Fitting a Line
• Classification Code Preparation
• Text Classification in Tensorflow
• The Neuron
• How does a model learn?
• The Neuron – Section Summary

16. Feedforward Artificial Neural Networks

• ANN – Section Introduction
• Forward Propagation
• The Geometrical Picture
• Activation Functions
• Multiclass Classification
• ANN Code Preparation
• Text Classification ANN in Tensorflow
• Text Preprocessing Code Preparation
• Text Preprocessing in Tensorflow
• Embeddings
• CBOW (Advanced)
• CBOW Exercise Prompt
• CBOW in Tensorflow (Advanced)
• ANN – Section Summary
• Aside: How to Choose Hyperparameters (Optional)

17. Convolutional Neural Networks

• CNN – Section Introduction
• What is Convolution?
• What is Convolution? (Pattern Matching)
• What is Convolution? (Weight Sharing)
• Convolution on Color Images
• CNN Architecture
• CNNs for Text
• Convolutional Neural Network for NLP in Tensorflow
• CNN – Section Summary

18. Recurrent Neural Networks

• RNN – Section Introduction
• Simple RNN / Elman Unit (pt 1)
• Simple RNN / Elman Unit (pt 2)
• RNN Code Preparation
• RNNs: Paying Attention to Shapes
• GRU and LSTM (pt 1)
• GRIJ and LSTM (pt 2)
• RNN for Text Classification in Tensorflow
• Pans-of-Speech (POS) Tagging in Tensorflow
• Named Entity Recognition (NER) in Tensorflow
• Exercise: Return to CNNs (Advanced)
• RNN – Section Summary

19. Setting Up Your Environment FAQ

• Pre-lnstallation Check
• Anaconda Environment Setup
• 3 lecturef Natural Language Processing in Python
• How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

20. Extra Help With Python Coding for Beginners FAQ

• 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

21. Effective Learning Strategies for Machine Learning FAQ

• How to Succeed in this Course (Long Version)
• Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
• Machine Learning and A1 Prerequisite Roadmap (pt 1)
• Machine Learning and A1 Prerequisite Roadmap (pt 2)

22. Appendix / FAQ Finale

• What is the Appendix?
• BONUS

Requirements of Natural Language Processing in Python:

• Python Installation:
• Ensure Python is installed on your system. It’s a free and widely used programming language, making it accessible to all.
• Decent Python Programming Skills:
• Familiarity with Python programming is recommended. The course assumes a basic understanding, allowing you to seamlessly grasp the concepts presented.
• Optional – Math Foundation:
• For a deeper comprehension, consider a basic understanding of linear algebra and probability. While not mandatory, it can enhance your ability to delve into the mathematical aspects covered in the course.
• Prepare for an enriching learning experience by meeting these simple requirements!

Target Audience Natural Language Processing in Python:

• Aspiring NLP Learners:
• Individuals eager to delve into the intricacies of Natural Language Processing (NLP) will find this course tailored to their learning needs.
• AI, ML, DL, or Data Science Enthusiasts:
• This course caters to those with a passion for artificial intelligence, machine learning, deep learning, or data science, providing a comprehensive exploration of these dynamic fields Natural Language Processing in Python.
• Advanced Learners Beyond Udemy Basics:
• Designed for learners who wish to surpass the fundamentals typically covered in beginner-only courses on platforms like Udemy, this course offers a more in-depth and advanced educational experience.
• Embark on this journey of knowledge, crafted for a diverse audience eager to excel in NLP and related domains!

Free on this educational adventure, and elevate your understanding of Natural Language Processing in Python. Uncover the synergy between coding and linguistics as you delve into the fascinating world of machine learning on howtofree.org!

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