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Our AI-Powered Curriculum
at a glance

Foundations of Generative AI

  • Introduction to Generative AI

    • Overview of generative AI concepts and history

    • Applications of generative AI across various domains

    • Introduction to Python and essential libraries for AI

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  • Machine Learning Fundamentals

    • Basics of machine learning, including supervised and unsupervised learning

    • Key algorithms and their applications

    • Data preparation techniques and exploratory data analysis (EDA)

Generative Models and Neural Networks

  • Neural Networks

    • Introduction to neural network architecture and training

    • Activation functions, loss functions, and regularization techniques

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  • Generative Models

    • Overview of generative models: Variational Autoencoders (VAEs)

    • Practical implementation and evaluation of VAEs

    • Understanding Generative Adversarial Networks (GANs) and their architecture

    • Training GANs, exploring variants (DCGAN, CycleGAN, StyleGAN), and practical applications

Advanced Applications and Ethical Considerations

  • Introduction to Generative AI

    • Overview of generative AI concepts and history

    • Applications of generative AI across various domains

    • Introduction to Python and essential libraries for AI

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  • Machine Learning Fundamentals

    • Basics of machine learning, including supervised and unsupervised learning

    • Key algorithms and their applications

    • Data preparation techniques and exploratory data analysis (EDA)

Comprehensive Curriculum in Generative AI

Background

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Fundamentals of AI and Machine Learning

  • Introduction to AI and ML

    • Overview of artificial intelligence and machine learning concepts

    • Applications of AI across various industries

    • Introduction to Python programming for AI

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  • Data Science Basics

    • Understanding data types and structures

    • Data collection, cleaning, and preprocessing techniques

    • Exploratory data analysis (EDA) and data visualization using libraries like Pandas and Matplotlib

Core Machine Learning Techniques

  • Supervised Learning

    • Key algorithms: linear regression, logistic regression, decision trees

    • Model evaluation metrics: accuracy, precision, recall, F1-score

    • Overfitting, underfitting, and model tuning techniques

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  • Unsupervised Learning

    • Clustering algorithms: K-Means, hierarchical clustering

    • Dimensionality reduction techniques: PCA and t-SNE

    • Anomaly detection and its applications

Advanced AI Concepts and Applications

  • Neural Networks and Deep Learning

    • Introduction to neural network architecture and training

    • Activation functions, loss functions, and backpropagation

    • Overview of deep learning frameworks: TensorFlow and PyTorch

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  • Specialized AI Applications

    • Natural Language Processing (NLP) basics and text analysis

    • Computer vision fundamentals and image processing techniques

    • Reinforcement learning concepts and its applications in real-world scenarios

Comprehensive Curriculum in CORE AI

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