Learn Natural Language Processing with Python

Learn Natural Language Processing with Python, available at $54.99, with 111 lectures, and has 1 subscribers.

You will learn about Computational Graphs PyTorch Basics Corpora, Tokens, and Types N-grams Simplest Neural Network Activation Functions Supervised Training Feed-Forward Networks The Multilayer Perceptron Model Evaluation and Prediction Convolutional Neural Networks Batch Normalization (BatchNorm) Network-in-Network Connections The CBOWClassifier Model Sequence Modeling Recurrent Neural Networks Intermediate Sequence Modeling Vanilla RNNs (or Elman RNNs) Advanced Sequence Modeling This course is ideal for individuals who are People who want to explore Data Science or People who want to explore Natural Language Processing or People who want to explore Artificial Intelligence or People who want to explore Neural Networks It is particularly useful for People who want to explore Data Science or People who want to explore Natural Language Processing or People who want to explore Artificial Intelligence or People who want to explore Neural Networks.

Enroll now: Learn Natural Language Processing with Python

Summary

Title: Learn Natural Language Processing with Python

Price: $54.99

Number of Lectures: 111

Number of Published Lectures: 40

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 40

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Computational Graphs

PyTorch Basics

Corpora, Tokens, and Types

N-grams

Simplest Neural Network

Activation Functions

Supervised Training

Feed-Forward Networks

The Multilayer Perceptron

Model Evaluation and Prediction

Convolutional Neural Networks

Batch Normalization (BatchNorm)

Network-in-Network Connections

The CBOWClassifier Model

Sequence Modeling

Recurrent Neural Networks

Intermediate Sequence Modeling

Vanilla RNNs (or Elman RNNs)

Advanced Sequence Modeling

Who Should Attend

People who want to explore Data Science

People who want to explore Natural Language Processing

People who want to explore Artificial Intelligence

People who want to explore Neural Networks

Target Audiences

People who want to explore Data Science

People who want to explore Natural Language Processing

People who want to explore Artificial Intelligence

People who want to explore Neural Networks

Welcome to the exciting world of Natural Language Processing (NLP) and Neural Networks! In this comprehensive course, you will embark on a journey to master the fundamentals of NLP and neural networks using the powerful combination of Python programming language and PyTorch framework. Whether you are a beginner or an experienced programmer, this course will equip you with the essential skills and knowledge to leverage the potential of NLP and neural networks for various applications.

Natural Language Processing (NLP) has emerged as a critical field within artificial intelligence, enabling computers to understand, interpret, and generate human language. Through a series of hands-on exercises and projects, you will delve into the core concepts of NLP, including text preprocessing, sentiment analysis, named entity recognition, part-of-speech tagging, and more. You will learn how to manipulate and analyze textual data using Python libraries such as NLTK (Natural Language Toolkit) and spaCy, gaining insights into the underlying structure of language.

Neural networks have revolutionized the field of machine learning, offering powerful tools for solving complex tasks. In this course, you will explore the foundations of neural networks, including perceptrons, feedforward networks, backpropagation, activation functions, and optimization algorithms. You will then delve into advanced neural network architectures such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformers, which are specifically designed to handle sequential data like text.

PyTorch has emerged as one of the leading deep learning frameworks, known for its flexibility, efficiency, and ease of use. Throughout this course, you will harness the capabilities of PyTorch to implement NLP models and neural networks from scratch. You will learn how to define network architectures, train models on large datasets, and evaluate their performance using various metrics. By the end of the course, you will have the confidence and proficiency to build cutting-edge NLP applications and neural network models using PyTorch.

Key Topics Covered:

1. Introduction to Natural Language Processing (NLP)

2. Text Preprocessing Techniques

3. Sentiment Analysis and Text Classification

4. Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging

5. Word Embeddings and Semantic Similarity

6. Introduction to Neural Networks

7. Perceptrons and Feedforward Networks

8. Backpropagation and Gradient Descent

9. Activation Functions and Optimization Algorithms

10. Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs)

11. Transformers for NLP Tasks

12. Introduction to PyTorch and its Ecosystem

13. Building NLP Models with PyTorch

14. Implementing Neural Networks with PyTorch

15. Training and Evaluating Deep Learning Models

Prerequisites:

This course is designed for individuals with a basic understanding of Python programming language and familiarity with machine learning concepts. While prior experience with deep learning or NLP is not required, a strong foundation in Python programming will be beneficial. Participants should also have a curiosity for exploring the intersection of language, artificial intelligence, and neural networks.

By the end of this course, you will be equipped with the skills and knowledge to tackle real-world NLP challenges and leverage the power of neural networks for a wide range of applications. Whether you aspire to pursue a career in data science, natural language processing, or artificial intelligence, this course will provide you with a solid foundation to achieve your goals. Join us on this exciting journey and unlock the potential of NLP and neural networks with Python and PyTorch!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Supervised Learning

Lecture 3: One-Hot Representation

Lecture 4: Term-Frequency (TF)

Lecture 5: TF-IDF

Lecture 6: Target Encoding and Computations

Lecture 7: Creating Tensors

Lecture 8: Tensor Size and Types

Lecture 9: Tensor Operations

Lecture 10: Joining, Slicing and Indexing

Lecture 11: Computational Graphs and Tensors

Chapter 2: Neural Network

Lecture 1: Perceptron The Simplest Neural Network

Lecture 2: Perceptron The Simplest Neural Network – 2

Lecture 3: Sigmoid

Lecture 4: Tanh

Lecture 5: ReLU

Lecture 6: Softmax

Lecture 7: Mean Squared Error Loss

Lecture 8: Categorical Cross-Entropy Loss

Lecture 9: Binary Cross-Entropy Loss

Lecture 10: Toy Data Construction

Lecture 11: Model Choosing and Loss Function

Lecture 12: Optimizer Choosing

Lecture 13: Gradient-Based Supervised Learning

Lecture 14: Classifying Sentiment of Restaurant Reviews with Yelp

Lecture 15: Creating Training, Validation, Testing

Lecture 16: PyTorchs Dataset Representation

Lecture 17: PyTorchs Dataset Representation – 2

Lecture 18: Vectorizer, DataLoader and Vocabulary

Lecture 19: Vectorizer, DataLoader and Vocabulary – 2

Lecture 20: Vectorizer, DataLoader and Vocabulary – 3

Lecture 21: Vectorizer

Lecture 22: Vectorizer – 2

Lecture 23: DataLoader

Lecture 24: Perception Classifier

Lecture 25: Training Routine

Lecture 26: Training Begins

Lecture 27: Training Loop

Lecture 28: Training Loop – 2

Lecture 29: Test Data Evaluation

Instructors

Tech Career World
Udemy Instructor

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