Sign up to save your podcasts
Or
Share 21: Why Scikit-Learn and Keras are Awesome for ML
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
21: Why Scikit-Learn and Keras are Awesome for ML
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
Thanks for listening! Please consider supporting this podcast from the link in the end.
View all episodes
By Sanket Gupta
5
55 ratings
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
Thanks for listening! Please consider supporting this podcast from the link in the end.