Data Science

  Gen AI Red Teaming Playbook Before you deploy your GenAI model… try breaking it. Sounds counterintuitive? It’s not. It’s called Red Teaming - and it's your last line of defense before things go wrong in production. - Prompt injection - Jailbreak attempts - Adversarial testing …these aren’t future risks. They’re happening now. That’s why I put together this Red Teaming Playbook - a visual guide for leaders in banking, insurance, and public sector to evaluate AI risks before deployment. Inside: - Threats to test - Tools like Rebuff, Guardrails-dot-ai, OpenAI Eval - 4-step process for safe AI Don’t wait for a PR disaster. Break your AI before someone else does.

  Unlocking the True Cost of Generative AI As organizations increasingly integrate generative AI into their operations, understanding the associated costs is crucial for effective budgeting and resource allocation. Here's a comprehensive breakdown of the various cost types involved in building and maintaining AI systems: GenAI Tools & Platform Access Costs Prompt Engineering Costs Inference Costs Fine-Tuning Costs Infrastructure Costs Data Management Costs Operational Costs AI Regulations Compliance Costs Talent Costs Change Management Costs By strategically managing these cost types, you can maximize the investment in generative AI and drive innovation forward. What are your thoughts on these cost factors? How are you addressing them?

 LLM Evaluation Guide Large Language Model (LLM) is the industry buzz word in recent years. It can understand human language and plays crucial roles in applications like chatbots, translations, and content creation. Evaluating LLMs is vital to ensure they produce accurate, relevant, and reliable outputs while minimizing biases and errors. Effective evaluation helps identify the strengths and weaknesses of these models, ensuring they perform well in real-world scenarios. Key metrics include BLEU and ROUGE for text quality, BERTScore and MoverScore for semantic similarity, and QuestEval for relevance and completeness. Proper evaluation guarantees that LLMs meet high standards and user expectations. Here are few dimensions on which LLMs can be evaluated. - Evaluating Generated Text Quality - Evaluating Semantic Similarity - Evaluating Factual Consistency - Evaluating Relevance and Completeness - Detecting Hallucinations - Evaluating User Preferences - No References Available What othe...

 Python Turtle Package “Turtle” is a python feature like a drawing board, which lets you command a turtle to draw all over it! You can use functions like turtle.forward(...) and turtle.left(...) which can move the turtle around. It was part of the original Logo programming language developed by Wally Feurzeig, Seymour Papert and Cynthia Solomon in 1967 Short Tutorial

 Data Viz - Part 1

Machine Learning Part 11 - Feature Selection Feature Selection

 Data Science Interview Questions

 Time Series Forecasting - Basics PDF:

 Six Sigma Basics PDF: SPC Made Easy: Referenced from Qi Macros

 Machine Learning - Books & Materials Why does it matter? With industry 4.0 transformation domain knowledge along with machine learning skills will present a killer combination What is Machine Learning? If you search internet you will end up getting hundreds of courses, mathematical theorems or hi-fi corporate jargons and stories about artificial intelligence, fascinating data science and dream jobs of future Say you want to get a phone. Years back you might have visited a store compared the specifications, price, color etc. and spent hours before making a decision. However now while enjoying the comfort of your home you give some inputs like budget, OS, camera etc. and you see the results on internet. The field of study with one goal of turning data to intelligent action or predictions is known as “Machine Learning” Free ML Books:- ML for beginners Machine Learning For Dummies ML Intro ML By Andrew NG Math for ML Pandas_1 Introduction to Machine Learning with Python Machine le...

Machine Learning - Part 9 Advanced Regression PDF: Video:  

 Deep Learning - Part 1 What will you learn? What is Artificial Neuron? How does it work What is math behind an articifical neuron? PDF: Video:

 Data Storytelling - Part 4 Data Storytelling is an compelling narrative crafted around and anchored by compelling data Data is nothing but numbers and characters until it is transformed to a story Lets see an industry use case of data story telling Video: PDF:

   Data Science Industry Use Case - P3 Data Science for Corrosion PDF: VIDEO:

Deep Learning Books & Materials   Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks The scalability of neural networks is key factor indicating the importance of Deep Learning (with more data and larger models, that in turn require more computation to train) In addition to scalability, another often cited benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning. Books & Materials: Neural Networks Intro Neural_Network and Deep_Learning Deep Learning Methods and Applications Deeplearning_notes Dive into Deep Learning DL Notes from Andrew Ng Course Machine Learning Neural and Statistical Classification Deep Learning Tutorial with Python AutoEncoders CNN_Cheatsheet RNN_cheatsheet Keras_Cheat_Sheet_Python_1 Keras_Cheat_Sheet_Python_2 Google Drive Link

 Data Science Industry Use Case - P1 Data Science for Water

 Data Science Books & Materials Image Source: UC Berkeley Data science: Data science is the practice of analysing raw data through a wide variety of disciplines and expertise areas to produce a holistic view and predict uncertainity. What can data science be used for? Anomaly detection (transaction fraud, disease, faulty sensor etc.) Classifications (spam detection) Forecasting (sales, revenue and customer retention) Pattern detection (weather patterns, financial market patterns, etc.) Recognition (facial, voice, text, etc.) Recommendations (recommend a movie, hotel etc.) Below are some of the useful / free books & materials that I collected from various online sources. it will be useful if you are looking to transition into data science field. Introduction to Data Science Data Science from Scratch Data Science for Business - Foster Provost & Tom Fawcett Data Analytics Data Cleaning Data Engineering Data Science Statistics Cheatsheet Data Science  - Algorithms-Ha...

 Data Storytelling - Part 3 Data Storytelling =  (Context + Audience + Visual) * Narrative Video Tutorial

AGILE SHIFT - SCRUM Scrum framework within which people can address complex adaptive problems, while productively and creatively delivering products of the highest possible value. Scrum is Lightweight Simple to understand Difficult to master Detailed Video Tutorial of Scrum Basics

AGILE SHIFT - PRINCE2 AGILE You will learn the following after going through below video and document Why Agile? Emerging Trends What is Agile? Prince 2 Agile - Basics Video Tutorial Quick Walkthrough Prince2 Agile: