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Building Advanced Deep Learning and NLP Projects

Name: Building Advanced Deep Learning and NLP Projects
Price: 59 USD
Author: Harsh Jain

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.

4.9

57 lessons

Created by Harsh Jain

Building Advanced Deep Learning and NLP Projects

What's included

57 lessons across 10 modules
10 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Explain CNN components (convolution, pooling, flattening, fully connected) for image classification
Build and train a Keras CNN to detect COVID-19 from X-ray images using data augmentation
Use Keras layers and callbacks (Dropout, Conv2D, EarlyStopping, ModelCheckpoint) to improve training
Apply transfer learning with ResNet50 to build and fine-tune an image classifier on a new dataset
Implement Markov chain text generation using frequency tables, probabilities, and sampling with context
Create word embeddings with TF-IDF/CountVectorizer and Word2Vec to compute word/sentence similarity
Develop RNN/GRU/LSTM models for sentiment analysis, Caesar cipher decryption, and emoji prediction with GloVe

Learning Roadmap

57 Lessons10 Quizzes

Welcome to the Course

Get familiar with advanced deep learning and NLP through hands-on projects and coding support.

Overview

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

Get started with building a COVID-19 detection system using CNNs and image data.

Introduction to Convolutional Neural Networks

Convolution

Pooling

Flattening and the Full Connection Operation

Key Functions

Project Creation: Part One

Project Creation: Part Two

Pop Quiz!

Assignments and Supplemental Reading Materials

Assignment Solution

Project: Building a Pokemon Classifier Using Transfer Learning

11 Lessons

Go hands-on with building a Pokemon classifier using transfer learning and ResNet50.

Project: Text Generation Using Markov Chains

7 Lessons

Apply your skills to build a text generator using Markov chains for dynamic text creation.

Word Embedding: Two Mini Projects

8 Lessons

Take a closer look at word embeddings, advanced NLP techniques, and practical mini projects.

Project: IMDB Reviews Sentiment Analysis

11 Lessons

Follow the process of building an RNN for IMDB sentiment analysis, covering architectures, LSTMs, embedding layers, model training, and assignments.

Project: Deciphering Text Using Character-Level RNNs

3 Lessons

Master the steps to build, train, and evaluate a character-level RNN model for text decryption.

Project: Emoji Predictor Using Transfer Learning in NLP

3 Lessons

Try out building an emoji predictor using GloVe, LSTMs, and transfer learning.

Final Exam

Look at all concepts in the final exam to verify your comprehension.

Take the Exam!

10.

Where to Go Next?

2 Lessons

Break apart project ideas and next steps for advancing deep learning and NLP skills.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Harsh Jain

Learn more about Harsh

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