/learning

Becoming 1% better at data science everyday

MIT LicenseMIT

learning

Learning Philosophy:

Develop a business acumen

Be able to frame a ML problem

Understand data ethics better

Be able to annotate data efficiently

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 import data from multiple sources

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 able to structure machine learning projects

Be able to utilize version control

Be familiar with a breadth of models and algorithms

Be able to implement models in scikit-learn

Be able to implement models in Tensorflow and Keras

Be able to implement models in PyTorch

Be able to apply unsupervised learning algorithms

Be able to implement computer vision models

Be able to implement NLP models

Be able to model graphs and network data

Be able to implement models for timeseries and forecasting

Be familiar with Reinforcement Learning

Be able to use managed ML services on the cloud

Be able to optimize performance metric

Be able to optimize models for production

Be able to deploy model to production

Be able to perform A/B testing

Be able to write unit tests

Be proficient in Python

Be familiar with compiled languages

Have a general understanding of other parts of the stack

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