25 Courses on Artificial Intelligence by IBM
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Gen AI Foundational Models for NLP & Language Understanding
Explain how to use one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.
Generative AI: Foundation Models and Platforms
Explore the features, capabilities, and applications of different generative AI platforms, such as IBM watsonx and Hugging Face.
Generative AI: Impact, Considerations, and Ethical Issues
Identify the ethical issues, concerns, and misuses associated with generative AI.
Generative AI Language Modeling with Transformers
Implement positional encoding, masking, attention mechanism, document classification, and create LLMs like GPT and BERT.
Machine Learning Introduction for Everyone
Differentiate between supervised and unsupervised machine learning.
Generative AI: Prompt Engineering Basics
Explore commonly used tools for prompt engineering to aid with prompt engineering.
AI Workflow: Machine Learning, Visual Recognition and NLP
Covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.
Machine Learning with Python
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression.
AI Workflow: Data Analysis and Hypothesis Testing
In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA).
Data Analysis with Python
Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data.