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

# Load the dataset
Now's the time to work with our real corpus. Click the download button below to get the dataset. This dataset contains the speech of the Honorable Prime Minister of India in English.

text_path = "train_corpus.txt"
def load_text(filename):
    with open(filename,encoding='utf8') as f:
        return f.read().lower()
    
text = load_text(text_path)
print('Loaded the dataset.')

# Understand sampling
Before moving forward, one more important concept needs to be addressed: sampling.
In simple words, sampling is the action or process of taking samples of something for analysis. Let’s understand sampling with the help of an example. Run the code cell below to see the output.

In this lesson, we will discuss sampling and build our Markov model.

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 Two

Load the dataset

Understand sampling