Machine Learning for Software Engineers
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 http://work.caltech.edu/telecourse.htmlWork 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.