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Link to coursera:  https://www.coursera.org/learn/generative-ai-for-everyone AI Supervised learning Labeling the output Performance depends on the amount of data Unsupervised learning Gnerative AI Reinforcement learning Large language models (LLM) Using the supervised learning to generate the text (learning from the repetitive sentences) LLM – > hundreds of billions of words – > that’s why the model can create a good performance Language is quite repetitive – > that is why it can generate the text LLM A new way to find information It can give you information Sometimes, it hallucinate Writing partner Finding information through LLM (but better to double checks) – perhaps web search might be better Generative AI General technology (for now) Useful for lots of things Work well with unstructured data (non-tubular data, for example, table form) Generating the image – > diffusion model by which labelling the text together with the image and repetitively do it step-by-step unt...

The process presented as part of the Google Data Analytics Certificate is one that will be valuable to you as you keep moving forward in your career: Data analytic way Ask: Business Challenge/Objective/Question Prepare: Data generation, collection, storage, and data management Process: Data cleaning/data integrity Analyze: Data exploration, visualization, and analysis Share: Communicating and interpreting results  Act:  Putting your insights to work to solve the problem Essential aspect of analytical skills: Curiosity: a desire to know more about something, asking the right questions Understanding context: understanding where information fits into the “big picture” Having a technical mindset: breaking big things into smaller steps Data design: thinking about how to organize data and information Data strategy: thinking about the people, processes, and tools used in data analysis The five key aspects to analytical thinking.  Your note Thinking analytically  - v...

 https://thewep.org/  (MCTQ) I know this from coursera:  Circadian clocks: how rhythms structure life Ludwig-Maximilians-Universität München (LMU) The course was conducted by two professors: https://pubmed.ncbi.nlm.nih.gov/?term=Roenneberg+T&cauthor_id=26196479 https://pubmed.ncbi.nlm.nih.gov/?term=Merrow+M&cauthor_id=34816105 The course goes from the big picture to the molecular level. Learning the new terms related to chronobiology. 

Google Data Analytics The process to do data analysis in order to drive the decision. Foundations Ask Ask the questions that we would like to be solved The question that would improve the current situation Ask question to define the problem Prepare Set the timeline and collecting the data Process Cleaning the data Checking the information Analyze Finding the pattern in data Finding the relationships and trends in data Share provide information of analysis Act make decision Use the analysis results Capstone Dimension of data analytics – there are three; pick one that is suit to the personality Machine learning – care about the model construction Statistic – using the number to describe the data Analytic – make sure either the model or interpretation makes sense Subject matter expert – a person who oversees whether the data analytic makes sense in the specific field. Life cycle of data analytic proposed by SAS -- Below is the information that I copied from Coursera to remind myself of th...