AI Fundamentals for Non-Data Scientists

• You will go in-depth to discover how AI is used to handle and interpret Big Data
• You will get a detailed look at the various ways and methods to create algorithms to incorporate into business with such tools as Teachable Machine and Tensor Flow.
• You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms
• By the end of this training, you will have learned different ways to use no-code tools (tools which will not include any coding), understand Deep Learning
• How to measure and review errors in your algorithms
• How to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive business activities
• Finally, you will learn how to implement what you have learned to the fields of Marketing & Finance

Big Data and Artificial Intelligence

In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analysed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.

Training and Evaluating Machine Learning Algorithms

In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms.

ML Application and Emerging Methods

In this module, you will take a look at Machine Learning within natural language processing and using generative modelling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning Tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm.

AI Applications in Marketing and Finance

In Marketing, you will delve into the impact of AI in marketing, and how it affects how customers interact with organization and its offerings. You will learn about how AI is disrupting retail and transforming the way that we conduct business in the digital age. You will have a firm understanding of how AI influences and also impact customer behaviour, and how you can take advantage of the myriad of ways AI can be applied to support your business and align with your customers.

In finance, you will learn how to mitigate fraud using AI systems. By examining various machine learning methods, you will discover different ways to analyse risk assessment using KPIs and the scientific method. You will then learn about corporate credit and the relationship between money borrowed, the price and availability of credit, as well corporate credit ratings and why and how that rating translates to risk.