AI Engineering Curriculum

A curriculum for those specialising in the automatic scaling and deployment of enterprise machine learning and artificial intelligence (AI).

Advanced Python 1

2019-10-18T03:24:37+00:00Categories: Level 2, Data Science Curriculum, Python, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This class builds on the introductory Python class. Jupyter Notebook advanced use and customisation is covered as well as configuring multiple environments and kernels. The Numpy package is introduced for working with arrays and matrices and a deeper coverage of Pandas data analysis and manipulation methods is provided including working with time series data. Data exploration and advanced visualisations are taught using the Plotly and Seaborne libraries.

Advanced Python 2

2019-10-18T03:25:47+00:00Categories: Data Science Curriculum Electives, Python, Level 3, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This class builds on the introductory Python class. Jupyter Notebook advanced use and customisation is covered as well as configuring multiple environments and kernels. The Numpy package is introduced for working with arrays and matrices and a deeper coverage of Pandas data analysis and manipulation methods is provided including working with time series data. Data exploration and advanced visualisations are taught using the Plotly and Seaborne libraries.

Advanced R 1

2019-10-18T03:26:29+00:00Categories: Level 2, Data Science Curriculum, tidyverse, Shiny, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This class builds on “Intro to R (+data visualisation)” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable. The course will also provide a deeper view of functional programming in R, which also allows cleaner and more powerful coding, as well as R Markdown, R Notebooks, and the shiny package for interactive documentation, browser-based dashboards and GUIs for R code.

Advanced R 2

2019-10-18T03:26:41+00:00Categories: Data Science Curriculum Electives, tidyverse, R, Level 3, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This course goes deeper into the tidyverse family of packages, with a focus on advanced data handling, as well as advanced data structures such as list columns in tibbles, and their application to model management. Another key topic is advanced functional programming with the purrr package, and advanced use of the pipe operator. Optional topics may include dplyr on databases, and use of rmarkdown and Rstudio notebooks.

Overcoming Information Overload with Advanced Practices in Data Visualisation

2019-10-24T04:46:56+00:00Categories: Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, AI Engineering Level 1, All Academy Courses|Tags: , , , , , , , |

In this workshop, we explore best practices in deriving insight from vast amounts of data using visualisation techniques. Examples from traditional data as well as an in-depth look at the underlying technologies for visualisation in support of geospatial analytics will be undertaken. We will examine visualisation for both strategic and operational BI.

Advanced Machine Learning Masterclass 1

2019-10-17T03:06:50+00:00Categories: Level 2, Data Science Curriculum, tidyverse, R, Data Engineering Curriculum, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.

Advanced Machine Learning Masterclass 2: Random Forests

2019-11-29T04:49:19+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, tidyverse, R, Data Engineering Level 2, Dr Eugene Dubossarsky, AI Engineering Curriculum, AI Engineering Level 2, All Academy Courses|Tags: , |

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.

Blockchain, Smart Contracts and Cryptocurrency

2019-10-25T01:54:25+00:00Categories: AI Engineering Curriculum Electives, Data Culture Electives, Data Science Curriculum Electives, Data Governance Curriculum Electives, Executive Curriculum Electives, Tristan Blakers, Introductory, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, All Academy Courses|Tags: , , , |

Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype. We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain. The course does not require a technical background.

Quantum Computing

2019-10-18T03:34:56+00:00Categories: AI Engineering Curriculum Electives, Data Science Curriculum Electives, Introductory, Dr Eugene Dubossarsky, Innovation & Tech (CTO) Curriculum, All Academy Courses|Tags: , , , |

This is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.

Advanced Deep Learning

2019-10-17T02:41:05+00:00Categories: Keras, Innovation & Tech (CTO) Curriculum Electives, Data Engineering Curriculum Electives, Tensorflow, Data Science Curriculum, Python, Dr Eugene Dubossarsky, AI Engineering Curriculum, Data Science Level 3, Data Engineering Level 3, AI Engineering Level 3, All Academy Courses, Innovation & Tech (CTO) Level 3|Tags: , |

This course provides a more rigorous, mathematically based view of modern neural networks, their training, applications, strengths and weaknesses, focusing on key architectures such as convolutional nets for image processing and recurrent nets for text and time series. This course will also include use of dedicated hardware such as GPUs and multiple computing nodes on the cloud. There will also be an overview of the most common available platforms for neural computation. Some topics touched in the introduction will be revisited in more thorough detail. Optional advanced topics may include Generative Adversarial Networks, Reinforcement Learning, Transfer Learning and probabilistic neural networks.