Aspire Journeys

Understanding the AI Landscape for Developers

  • 2 Courses | 2h 56m 54s
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In this Journey, you will begin by exploring the landscape of Artificial Intelligence (AI) by understanding its history and development, with a special focus on Deep Learning and Generative AI. You will learn about Symbolic and Subsymbolic AI and the different sub-branches within contemporary AI such as computer vision, natural language processing and reinforcement learning. You will also learn about the transformer, the development of the GPT (Generative Pretrained Transformer) series of models and how applications like ChatGPT are built. This journey is a gateway to Generative AI for managers, engineers and developers alike. It provides a holistic overview for any team interested in working with APIs of Large Language Models and improving their efficiency with Generative AI.

Track 1: Introduction to the AI Landscape & GPT

In this track of the Understanding the AI Landscape for Developers journey, the focus will be on Symbolic and Subsymbolic AI and the different sub-branches within contemporary AI such as computer vision, natural language processing and reinforcement learning. You will also learn about the transformer, the development of the GPT (Generative Pretrained Transformer) series of models and how applications like ChatGPT are built.

  • 2 Courses | 2h 56m 54s

COURSES INCLUDED

Artificial Intelligence and Machine Learning
This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions.
11 videos | 1h 36m has Assessment available Badge
Deep Learning and Neural Networks
Deep learning and neural networks have revolutionized various fields by enabling computers to automatically learn complex patterns from data. This led to breakthroughs in areas such as image recognition, natural language processing (NLP), and autonomous driving. In this course, you will compare and contrast traditional machine learning (ML) and deep learning models. You will see how deep learning models excel in automated feature extraction from raw data, tackling complex tasks with the power of vast datasets. You will explore the fundamental unit of deep learning, the neuron, and understand how it works. Next, you will explore the diverse neural network architectures designed for specific data types. You will learn how convolutional neural networks (CNNs) extract features from images and how recurrent neural networks (RNNs) are able to extract relationships in time-series data. Finally, you will explore how neural networks handle natural language processing. You will learn how attention-based models help models focus on crucial parts of the input data for enhanced predictions and how generative adversarial networks (GANs) work. You will also explore reinforcement learning, a machine learning technique where agents navigate uncertain environments to maximize rewards.
11 videos | 1h 20m has Assessment available Badge

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