/learning

Becoming 1% better at data science everyday

MIT LicenseMIT

learning

Learning Philosophy: Master Adjacent Disciplines, The Power of Tiny Gains, T-shaped skills

Develop a business acumen

Be able to frame a problem as ML problem

Be able to manipulate data with Numpy

Be able to manipulate data with Pandas

Be able to manipulate data in spreadsheets

Be able to manipulate data in databases

Be able to use the command line

Be able to perform feature engineering

Be able to experiment in notebook

Be able to visualize data

Be able to to read research papers

Be able to model problems mathematically

Be familiar with a breadth of models and algorithms

Be able to structure machine learning projects

Be able to implement computer vision models

Be able to implement NLP models

Be able to model graphs and network data

Be able to apply unsupervised learning algorithms

Be able to implement models for timeseries and forecasting

Be familiar with Reinforcement Learning

Be able to implement ML models with scikit-learn

Be able to implement neural networks in Tensorflow and Keras

Be able to implement models in PyTorch

Be able to implement edge inference

Be able to use managed ML services on the cloud

Be able to optimize model performance

Be able to serve and scale ML services

Be able to perform A/B test

Be able to write unit tests

Be proficient in Python

Be familiar with compiled languages

Be able to utilize version control

Be able to implement APIs and services

Be familiar with fundamental Computer Science concepts

Be able to apply proper software engineering process

Be able to efficiently use a text editor

Be able to communicate and collaborate well

Be familiar with the hiring pipeline

Broaden Perspective