# Intro and Resources

* Book: <https://github.com/oreilly-japan/deep-learning-from-scratch>
* **kaggle learning resource** <https://www.kaggle.com/learn/overview>
  * **Python** Learn the most important language for data science.
  * **Intro to Machine Learning** Learn the core ideas in machine learning, and build your first models.
  * **Intermediate Machine Learning** Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful.
  * **Data Visualization** Make great data visualizations. A great way to see the power of coding!
  * **Pandas** Solve short hands-on challenges to perfect your data manipulation skills.
  * **Feature Engineering** Discover the most effective way to improve your models.
  * **Deep Learning** Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.
  * **Intro to SQL** Learn SQL for working with databases, using Google BigQuery to scale to massive datasets.
  * **Advanced SQL** Take your SQL skills to the next level.
  * **Geospatial Analysis** Create interactive maps, and discover patterns in geospatial data.
  * **Microchallenges** Solve ultra-short challenges to build and test your skill.
  * **Machine Learning Explainability** Extract human-understandable insights from any machine learning model.
  * **Natural Language Processing** Distinguish yourself by learning to work with text data

## [HYLee](http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML20.html)

## Installation

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{% embed url="<https://github.com/aron3312/embedding2lm>" %}
