Chapter One (1.1) machine learning Algorithm Engineer Skill Tree _ machine learning

First, the machine learning algorithm engineers need to master the skills

Machine Learning algorithm engineers need to master skills including

(1) Basic data structure and algorithm tree and correlation algorithm graph and correlation algorithm hash table and correlation algorithm matrix and correlation algorithm

(2) Probability and statistical basis large number theorem central limit theorem common probability distribution hypothesis calibration theory maximum posteriori theory maximum likelihood theory em algorithm Bayesian bayes classification error rate

(3) Machine learning theory

3.1 Unsupervised learning mixed Gaussian model hierarchical clustering DBSCAN k-measn PCA SVD Word2vec

3.2 Supervised learning bagging decision Tree native Bayes KNN FM LR

3.3 Theory of basic theories of the regularization of information-theoretic VC Dimension

(4) Feature processing Feature selection processing feature normalization feature discretization feature crossover

(5) Development of language and development tools large data development tools (Storm, Spark, Hadoop) stand-alone development tools (NumPy, Sk-learn, Pandas, LIBSVM, xgboost) development language (Scala, R, Python)

(6) Basic development capability code cleanliness, readability and maintainability stability, performance, robustness tuning capabilities logical abstract Reuse Unit test

(7) Architecture Design machine learning related service Architecture Data Warehouse