Deep Learning with Python and Keras: Build a Model For Sentiment Analysis

سرفصل های دوره

Learn to apply sentiment analysis to your problems through a practical, real world use case. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through the process of building and training a RNN to do sentiment analysis, including validating your results. Go over how to preprocess text for sentiment analysis, as well as approaches you can use and challenges you may encounter. Get set up with Google Colab and import Python modules and loading data, then learn how to analyze word lengths, clean and preprocess text, and visualize text with word clouds. Explore feed-forward neural networks, then dive into configuring, training, and evaluating your dense neural network (DNN). Plus, learn how to train RNNs and LSTNs.

01 - Introduction

01 - An overview of sentiment analysis

02 - Prerequisites

02 - 1. Overview of Sentiment Analysis

01 - Preprocessing text for sentiment analysis

02 - Word vector encodings and word embeddings

03 - Types of sentiment analysis

04 - Approaches and challenges in sentiment analysis

03 - 2. Cleaning and Preprocessing Text Data

01 - Getting set up with Google Colab

02 - Importing Python modules and loading data

03 - Analyzing word lengths across sentiment categories

04 - Cleaning and preprocessing text

05 - Visualizing text using word clouds

04 - 3. Sentiment Analysis Using Dense Neural Networks

01 - Feed-forward neural networks

02 - Splitting data into training test and validation sets

03 - Representing text using count vectorization

04 - Configuring the dense neural network (DNN)

05 - Training and evaluating the DNN

06 - Configuring the count vectorizer as a model layer

07 - Representing text using TFIDF vectorization

08 - Training and evaluating the model

09 - Representing text using integer sequences

10 - Training ADNN using embeddings

05 - 4. Sentiment Analysis Using Recurrent Neural Networks

01 - Recurrent neural networks

02 - Long memory cells

03 - The LSTM and GRU cells

04 - Training a recurrent neural network

05 - Training an LSTM network

06 - Serializing a model to disk and loading the model

06 - Conclusion

01 - Summary and next steps

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تولید کننده: LinkedIn Learning (Lynda)