In this episode, we talk about why the two libraries Scikit-Learn and Keras are great for machine learning. These two libraries combined with Pandas form the 3 core libraries in Python for a data scientist today. 

We cover things like:

1)  Data Exploration and data cleaning - how Pandas and Jupyter notebooks provide a good way to get started here.
2) Data Transformation - how Scikit-Learn provides many useful functions like train_test_split, Scalers, PCA etc.
3) Data Fitting - how Scikit-Learn provides good shallow models and Keras provides great support to quickly get started with neural networks.

We also cover various tidbits on things to take note in building ML pipelines and preparing models to be deployed in production, so tune into the episode to find out!

Fantastic Resources:
1) Book by head of Youtube DS team Aurelien Geron: https://www.amazon.com/dp/1492032646/?tag=omnilence-20
This is one of the best book I have read on this topic as it covers practical tips incl. Scikit-Learn API etc.
2) Developing Scikit-Learn estimators: https://scikit-learn.org/stable/developers/develop.html
3) Guide to Keras Sequential API: https://keras.io/getting-started/sequential-model-guide/
4) Guide to Keras Functional API: https://keras.io/getting-started/functional-api-guide/
5) My previous episode on Pandas: https://podcasts.apple.com/us/podcast/17-why-pandas-is-the-new-excel/id1453716761?i=1000454831790

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