(Immediate Start) Artificial Intelligence

3 weeks ago

Tiruchchirappalli TN IN Jobted IN C2 Full time

Core Foundational Topics These subjects establish the necessary groundwork for understanding AI Introduction to AI Definition and History Understanding what AI is its major milestones and its different categories e g Narrow AI General AI AI Applications Exploring real-world use cases across industries like healthcare finance and autonomous systems Mathematics and Statistics Linear Algebra and Calculus Essential for understanding the mechanics of many AI algorithms especially in Deep Learning Probability and Statistics Crucial for data analysis model evaluation and understanding the uncertainty in predictions Programming Fundamentals Programming Languages Python is the dominant language with libraries like NumPy Pandas and Scikit-learn being key tools Data Structures and Algorithms Learning how to efficiently store process and manipulate the large datasets AI systems rely on Key AI Sub-fields The majority of an AI course is dedicated to its major sub-fields which focus on different types of intelligent behavior Machine Learning ML The core of modern AI teaching computers to learn from data without explicit programming Supervised Learning Training models on labeled data for tasks like Classification e g spam detection and Regression e g price prediction Unsupervised Learning Finding hidden patterns in unlabeled data using techniques like Clustering e g market segmentation Ensemble Techniques Combining multiple models to improve prediction accuracy Deep Learning DL A subset of ML that uses multi-layered Neural Networks to process complex data Convolutional Neural Networks CNNs Used primarily for image recognition and computer vision Recurrent Neural Networks RNNs and Transformers Used for sequential data like text and time series Generative AI Learning about models that can create new content such as Large Language Models LLMs for text generation and image generation models Shutterstock Natural Language Processing NLP Enabling computers to understand interpret and generate human language Topics include text representation sentiment analysis machine translation and building chatbots Computer Vision Allowing machines to see and interpret visual information from images and videos Topics include object detection image classification and facial recognition Reinforcement Learning RL Training an agent to make a sequence of decisions in an environment to maximize a reward often used in robotics and gaming Practical and Ethical Skills Successful AI professionals need more than just algorithms they need to apply them responsibly AI Tools and Frameworks Hands-on training with industry-standard libraries like TensorFlow and PyTorch and cloud platforms like Google Cloud AWS or Azure Data Handling Learning skills like Data Wrangling cleaning and preparing data Feature Engineering creating better inputs for models and Data Visualization Practical Projects Capstone Projects Applying learned knowledge to solve real-world problems which is crucial for building a professional portfolio AI Ethics and Responsible AI Discussing the societal impact fairness bias transparency and accountability of AI systems to ensure they are developed and used safely and justly Job Type Fresher Pay From 15 000 00 per month Work Location In person

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