This course is an intuitive, hands-on introduction to ai, data science and machine learning, it's your artificial intelligence 101. The training focuses on fundamentals and key skills, leaving you with a deep understanding of the core concepts of ai and data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. As our leading course, it has transformed the artificial intelligence (AI), machine learning (ML) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it.
This R training course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work. R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces.
Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.
With big data expert and author Jeffrey Aven. Learn how to develop applications using Apache Spark. The first module in the “Big Data Development Using Apache Spark” series, this course provides a detailed overview of the spark runtime and application architecture, processing patterns, functional programming using Python, fundamental API concepts, basic programming skills and deep dives into additional constructs including broadcast variables, accumulators, and storage and lineage options. Attendees will learn to understand the Apache Spark framework and runtime architecture, fundamentals of programming for Spark, gain mastery of basic transformations, actions, and operations, and be prepared for advanced topics in Spark including streaming and machine learning.