This Module covers fundamental Python, data manipulation using NumPy and pandas with visualization tools like Matplotlib and Seaborn, and an introduction to machine learning with scikit-learn, including data preprocessing, model creation, evaluation, and a practical mini-project.
This Module covers essential machine learning concepts, supervised and unsupervised techniques, popular algorithms like regression, decision trees, logistic regression, support vector machines, and clustering. It includes data preprocessing, model evaluation, dimensionality reduction, a glimpse into neural networks, hands-on Scikit-Learn exercises, and a capstone project for practical application, offering a foundational understanding of machine learning for beginners.
This Module explores ChatGPT and Google’s BERT models, covering their architectures, applications, hands-on exercises with ChatGPT, BERT’s bidirectional encoding, customization techniques, ethical considerations, a comparative analysis, and future trends in generative AI.
This Module covers key aspects of generative models, including architecture, implementation, customization, ethics, applications, and future trends. It explores models like ChatGPT, GANs, and VAEs, offering practical exercises for text, image, and sequence generation, ethical considerations, real-world applications, and insights into emerging trends, condensing essential knowledge for developers entering the field of generative AI.
Deepika Sharma
Developer