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No pre-requisite. Certified Applied AI Professional (CAAI) is suitable for anyone who is interested in Applied Artificial Intelligence and does not have any prior technological experience
Participants are required to attempt an examination upon completion of the course. This exam tests a candidate’s knowledge and skills related to Applied Artificial Intelligence based on the syllabus covered
Module 1 Introduction to Applied Artificial Intelligence
Applied artificial intelligence (AI) is a branch of computer science that focuses on creating practical solutions using machine learning and other AI techniques. It involves developing and implementing algorithms and models that can learn from and make predictions based on data.
Module 2 Deep Dive into Python Programming for Applied Artificial Intelligence
Python is used extensively in various AI applications such as natural language processing, computer vision, and predictive analytics. Its popularity among data scientists and AI developers has led to the creation of many useful libraries and tools, making Python an essential language for applied AI development.
Module 3 Data Pre-processing and Cleaning for Applied Artificial Intelligence
Data pre-processing and cleaning are essential steps in the development of applied AI models. It involves preparing and organizing raw data to ensure that it is suitable for analysis and use in machine learning algorithms.
Module 4 Machine Learning Regression, Classification and Clustering Techniques
Regression, classification, and clustering are the three main techniques used in ML. Regression is a method for predicting continuous numerical output based on input variables, while classification is used for predicting categorical output based on input variables.
Module 5 Deep Learning in Applied Artificial Intelligence
Deep learning techniques are widely used in applied AI because they can achieve state-of-the-art performance in tasks such as image and speech recognition, natural language processing, and recommendation systems. Examples of deep learning architectures include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).
Module 6 Natural Language Processing (NLP) in Applied Artificial Intelligence
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is widely used in applied AI, particularly in fields such as virtual assistants, chatbots, and sentiment analysis.
Module 7 Computer Vision (CV) in Applied Artificial Intelligence
CV is used in applied AI to solve problems such as object recognition, image segmentation, and facial recognition. CV involves several techniques, including image preprocessing, feature extraction, and deep learning-based approaches such as Convolutional Neural Networks (CNNs).
Certified Applied AI Professional (CAAI) involves extensive practical / hands-on exercises, rigorous usage of real-time case studies, role playing and group discussion