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       Applied AI from Scratch in Python培訓

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)