1、培訓(xùn)過(guò)程中，如有部分內(nèi)容理解不透或消化不好，可免費(fèi)在以后培訓(xùn)班中重聽(tīng)；
        2、課程完成后,授課老師留給學(xué)員手機(jī)和Email,保障培訓(xùn)效果,免費(fèi)提供半年的技術(shù)支持。
        3、培訓(xùn)合格學(xué)員可享受免費(fèi)推薦就業(yè)機(jī)會(huì)。

• Supervised learning: classification and regression
  Machine Learning in Python: intro to the scikit-learn API
  linear and logistic regression
  support vector machine
  neural networks
  random forest
  Setting up an end-to-end supervised learning pipeline using scikit-learn
  working with data files
  imputation of missing values
  handling categorical variables
  visualizing data
  Python frameworks for for AI applications:
  TensorFlow, Theano, Caffe and Keras
  AI at scale with Apache Spark: Mlib
  Advanced neural network architectures
  convolutional neural networks for image analysis
  recurrent neural networks for time-structured data
  the long short-term memory cell
  Unsupervised learning: clustering, anomaly detection
  implementing principal component analysis with scikit-learn
  implementing autoencoders in Keras
  Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g.
  image analysis
  forecasting complex financial series, such as stock prices,
  complex pattern recognition
  natural language processing
  recommender systems
  Understand limitations of AI methods: modes of failure, costs and common difficulties
  overfitting
  bias/variance trade-off
  biases in observational data
  neural network poisoning
  Applied Project work (optional)