Gain insights into advanced deep learning and NLP by building 12 real-world projects using tools like TensorFlow and scikit-learn. Enhance your portfolio with industry-relevant skills.

dockerfile.tar.gz

python3

spa-job

In this course, you'll not only learn advanced deep learning concepts, but you'll also practice building some advanced deep learning and Natural Language Processing (NLP) projects. By the end, you will be able to utilize deep learning algorithms that are used at large in industry.

 

This is a project-based course with 12 projects in total. This will get you used to building real-world applications that are being used in a wide range of industries. You will be exposed to the most common tools used for machine learning projects including: NumPy, Matplotlib, scikit-learn, Tensorflow, and more.

 

It’s recommended that you have a firm grasp in these topic areas: Python basics, Numpy and Pandas, and Artificial Neural Networks.

 

Once you’re finished, you will have the experience to start building your own amazing projects, and some great new additions to your portfolio.

Building Advanced Deep Learning and NLP Projects

## Introduction to the project
Welcome to this project. We will build an RNN based model and perform sentiment analysis on the IMDB movie reviews dataset. In this project, we will be using a simple RNN model and an embedding layer, which we discussed in the previous lessons.

For this project, we will be using the dataset provided by `Keras`. This dataset is already preprocessed, and the text (reviews) is already converted to its numeric representation.

Let's start our project by first loading the dataset.

## Load the dataset
We will need to have `Keras` installed for this project. You should have `Keras` already installed if you made it to this chapter. 

# Introduction to the project
Welcome to this project. We will build an RNN based model and perform sentiment analysis on the IMDB movie reviews dataset. In this project, we will be using a simple RNN model and an embedding layer, which we discussed in the previous lessons.

For this project, we will be using the dataset provided by `Keras`. This dataset is already preprocessed, and the text (reviews) is already converted to its numeric representation.

Let's start our project by first loading the dataset.

# Load the dataset
We will need to have `Keras` installed for this project. You should have `Keras` already installed if you made it to this chapter. 

In this lesson, we will be starting with the project on IMDB movie reviews analysis and perform some preprocessing tasks.

Welcome to the Course

Project: Build a COVID-19 Detection System Using X-Rays

Project: Building a Pokemon Classifier Using Transfer Learning

Project: Text Generation Using Markov Chains

Word Embedding: Two Mini Projects

Project: IMDB Reviews Sentiment Analysis

Project: Deciphering Text Using Character-Level RNNs

Project: Emoji Predictor Using Transfer Learning in NLP

Final Exam

Where to Go Next?

Project Creation: Part One

Introduction to the project

Load the dataset