Applied AI from Scratch in Python

There are no specific requirements needed to attend this course.

This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 

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)