AI & Machine Learning

AI should be without prejudice — and that’s down to the developers and coders:

• How to get started by developing your own very simple text cleaning tools.

• How to take a step up and use the more sophisticated methods in the NLTK library.

• How to prepare text when using modern text representation methods like word embeddings.

You cannot go straight from raw text to fitting a  machine learning or deep learning model.

You must clean your text first, which means splitting it into words and handling punctuation and case.

In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods really depends on your natural language processing task.

In this tutorial, you will discover how you can clean and prepare your text ready for modeling with machine learning.

After completing this tutorial, you will know:

1. How to Clean Text for Machine Learning with Python

2. AI ethics: Time to move beyond a list of principles

3. AI bias: It is the responsibility of humans to ensure fairness

4. AI & Machine Learning

5. Gartner: debunking five artificial intelligence misconceptions

6. Data Science University

7. Educate a New Generation of AI Developers

8. Top 8 Sources For Machine Learning and Analytics Datasets

9. How It Feels to Learn Data Science in 2019

10. Federated Learning: The Future of Distributed Machine Learning

11. How to Get Better Deep Learning Results (7-Day Mini-Course)

12. 3 Great Data Science Books for Aspiring Data Scientists

13. How to succeed with AI

14. Neural Networks: Tricks of the Trade Review

15. Learn Methods to Install and Use TensorFlow in Ubuntu

16. When to use different machine learning algorithms: a simple guide

17. Why are Scikit-learn machine learning models not as widely used in industry as TensorFlow or PyTorch?

Keywords: python, machine learning,deep learning,data science, artificial intelligence ,neural network ,education , TensorFlow