Natural Language Processing

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About the Course

WeCloud’s Natural Language Processing course offers a comprehensive introduction to the field of NLP, equipping students with the knowledge and skills to analyze, understand, and generate human language using computational methods. Participants will explore various NLP techniques, from basic text processing to advanced deep learning models, such as recurrent neural networks (RNNs) and transformers. The course emphasizes practical implementations using popular libraries and frameworks, enabling students to work on real-world NLP applications. By the end of the course, students will be prepared to develop and evaluate NLP solutions for diverse tasks, including text classification, sentiment analysis, and machine translation.

Curriculum

  • Module 1: Logistic Regression

    Overview:

    This module demonstrates the fundamentals of logistic regression and its application in binary classification tasks.

    Topics to Cover:

    • What is Logistic Regression?
    • Understand the predictions of Logistic Regression
    • Implement a Logistic Regression

  • Module 2: Neural Networks

    Overivew:

    This module dives into neural network (NN) mathematics and implementation, including hands-on work with PyTorch.

    Topics to Cover:

    • Mathematical calculations involved in Neural Network
    • Creating NN from scratch
    • Implement NN with PyTorch

  • Module 3: NLP Basics

    Overview:

    This module teaches the basic text processing techniques using NLTK and Spacy, along with regular expressions.

    Topics to Cover:

    • What is NLTK?
    • Introduction to text processing
    • Traditional NLP model using Spacy
    • Application of Regular Expression
    • Creating NLP ML Models

  • Module 4: Language Representation

    Overview:

    This module explores various techniques for representing text data, including TF-IDF, word co-occurrence, GloVe, and Word2Vec.

    Topics to Cover:

    • Application of various language processing
    • Applying TD-IDF, Word Co-Occurency, and Glove
    • What is Word2Vec?
    • Working with Gensim Models

  • Module 5: Traditional ML for NLP

    Overview:

    This module examines various NLP tasks and traditional machine learning algorithms for solving them.

    Topics to Cover:

    • Understand the different NLP tasks
    • Applying different encoding methods with Random Forest
  • Module 6: Neural Network for NLP

    Overview:

    This module studies the application of RNNs, LSTMs, and GRUs in NLP tasks, including their architecture and functionality.

    Topics to Cover:

    • Understanding the basic NN and RNN models
    • Applying single-layered and multi-layered RNN
    • Understanding LSTM and GRU
    • Applying LSTM and GRU model with single and multi-layers

  • Module 7: Bidirectional LSTM for NLP

    Overview:

    This module teaches about bidirectional LSTMs and their advantages for NLP applications.

    Topics to Cover:

    • Understand Bidirectional LSTM models
    • Applying different encoding methods to Bidirectional LSTM models

  • Module 8: Evaluating NLP Models

    Overview:

    This module examines evaluation metrics for different NLP tasks, including loss functions and performance measures.

    Topics to Cover:

    • What are the evaluation metrics?
    • Comparing the different metrics and their applications
    • Examine various many to many metrics

Learning Outcomes

By the end of this course, participants will be able to:

  • Explain the core concepts and techniques in NLP, including text processing and language representation.
  • Implement basic and advanced machine learning algorithms for NLP tasks using Python libraries.
  • Develop and fine-tune neural network models for various NLP applications, including RNNs and LSTMs.
  • Evaluate NLP models using appropriate metrics and loss functions tailored to specific tasks.
  • Apply NLP techniques to solve real-world problems, such as sentiment analysis and text classification.

Tools

Python
Jupyter
Natural Language Processing
$499.00
what you will get
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FAQ

Frequently asked questions about the bootcamp

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

You can get support in multiple ways:

  • TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
  • Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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