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Learn Python stack: scipy, numpy, pandas, scikit-learn, jupyter, matplotlib/seaborn. Learn machine learning tools: XGBoost, Scikit-learn, Keras, Vowpal Wabbit.
by BickNowstrom 10y ago
Learn Python stack: scipy, numpy, pandas, scikit-learn, jupyter, matplotlib/seaborn.
Learn machine learning tools: XGBoost, Scikit-learn, Keras, Vowpal Wabbit.
Do data science competitions: Kaggle, DrivenData, TopCoder, Numerai.
Take these courses: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning http://work.caltech.edu/telecourse.html http://work.caltech.edu/telecourse.html
Work on soft skills: Business, management, visualization, reporting.
Do at least one real-life data science project: Open Data, Journalism, Pet project.
Contribute to community: Create wrappers, open issues/pull reqs, write tutorials, write about projects.
Read: FastML, /r/MachineLearning, Kaggle Forums, Arxiv Sanity Preserver.
Implement: Recent papers, older algorithms, winning solutions.
As a software engineer you have a major advantage for applied ML: You know how to code. AI is just Advanced Informatics. If you want to become a machine learning researcher... skip all this and start from scratch: a PhD. Else: Learn by doing. Only those who got burned by overfit, will know how to avoid it next time.