Artificial intelligence

What Will You Learn?

• By the end of this course, you will have a strong understanding of AI concepts, algorithms, and tools. You’ll be prepared to apply AI techniques to solve real-world problems and contribute to innovation in fields such as healthcare, finance, robotics, and more. Here’s a breakdown of what you will learn:
• Introduction to Artificial Intelligence (AI)
• Understand the history and evolution of AI.
• Explore the various types of AI: narrow AI, general AI, and superintelligence.
• Learn about AI’s real-world applications in fields like healthcare, transportation, and entertainment.
• Mathematics for AI
• Build a solid foundation in the mathematics needed for AI, including linear algebra, calculus, probability, and statistics.
• Learn how these mathematical concepts are applied in AI algorithms and machine learning models.
• Machine Learning Fundamentals
• Learn the core principles of machine learning, including supervised, unsupervised, and reinforcement learning.
• Work with popular algorithms like Linear Regression, Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).
• Deep Learning and Neural Networks
• Dive into deep learning and understand the architecture of Neural Networks.
• Learn how to build Convolutional Neural Networks (CNNs) for image recognition, Recurrent Neural Networks (RNNs) for sequence data, and Generative Adversarial Networks (GANs) for data generation.
• Gain hands-on experience using libraries like TensorFlow and Keras to build deep learning models.
• Natural Language Processing (NLP)
• Explore how AI interacts with human language through NLP techniques.
• Learn about text preprocessing, tokenization, part-of-speech tagging, and sentiment analysis.
• Work with NLP libraries such as NLTK and spaCy to analyze and generate text.
• Reinforcement Learning
• Understand the concept of reinforcement learning and how agents learn to make decisions in an environment.
• Learn about key algorithms like Q-learning, Deep Q Networks (DQN), and Policy Gradients.
• Apply reinforcement learning to real-world challenges such as game playing and robotics.
• AI in Robotics
• Learn how AI is used to power robots for autonomous decision-making, navigation, and interaction with their environment.
• Gain knowledge of sensors, actuators, and control algorithms used in robotics.
• AI Ethics and Bias
• Understand the ethical implications of AI technologies, including fairness, transparency, and privacy concerns.
• Learn how to mitigate bias in AI models and ensure that AI systems are ethical and responsible.
• AI in Computer Vision
• Learn how AI is used in image processing and analysis, including object detection, facial recognition, and image segmentation.
• Work with OpenCV and TensorFlow to build computer vision applications.
• AI Deployment and Production
• Learn how to deploy AI models into production using tools like Flask, Docker, and Kubernetes.
• Explore cloud platforms like AWS and Google Cloud to host AI applications.
• Capstone Project
• Apply all the concepts learned in the course by working on a real-world AI project that addresses a problem in industries like healthcare, finance, or entertainment.
• Develop an AI-powered solution and deploy it as a fully functional application.
• Gnanavasi Technology provides a comprehensive learning experience designed to take you from foundational AI concepts to advanced applications. Whether you're looking to build a career in AI or enhance your existing skills, this course will equip you with the practical knowledge and hands-on experience you need to succeed in the field.