Artificial Intelligence

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.
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• 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