IBM designed course on Python for data science Master Python scripting Hands-on data analysis using Jupyter Lab.

Introduction to data science and its practical applications Grasp the essentials of NumPy Investigation into array indexing and slicing techniques Application of linear algebra principles in data analysis Calculation of central tendency and dispersion measures Explanation of null and alternative hypotheses Exploration of various hypothesis testing methods including Z-test and T-test Understanding the concept of ANOVA (Analysis of Variance) Utilization of Pandas for data loading, indexing, reindexing, and merging

Elevate ML skills with deep learning Learn TensorFlow and Keras Understand deep learning concepts Construct artificial neural networks Navigate data abstraction layers Unlock data potential for AI advancements

Transformers' significance in modern AI Neural networks' suitability for generative tasks Differentiate generative model types: VAEs, GANs, transformers, autoencoders Appropriate scenarios for diverse generative AI models Assess attention mechanisms' efficacy in generative tasks Analyze GPT and BERT, contrasting their architectural goals in generative AI Langchain and Workflow Design Advanced Prompt Engineering Techniques LLM Application Development LLM Fine-Tuning and Customization