Fourth Industrial Revolution

Summary : Machine learning algorithms let computers learn from data to make predictions and discover patterns. This post explains the main algorithm types, the typical workflow, and how to choose the right approach for your problem. What Are Machine Learning Algorithms? Machine learning algorithms are sets of procedures a computer follows to learn from data. Instead of being explicitly programmed for every scenario, these algorithms identify patterns, make predictions, and improve as they see more data. The goal is to build models that generalize from past examples to new, unseen situations. 1. Supervised Learning In supervised learning, the training data includes inputs and the correct outputs, known as labels. The algorithm learns a mapping from inputs to outputs so it can predict labels for new examples. Examples : Linear regression — predicts continuous values, such as house prices. Logistic regression and support vector machines — common for classification task...

Summary : An end-to-end AI/ML lifecycle transforms data into production-ready models. This post explains development, fine-tuning, deployment, and continuous optimization with practical steps to keep models accurate, efficient, and reliable. The End-to-End AI/ML Model Lifecycle: From Concept to Continuous Improvement Building useful AI and machine learning systems means moving through a clear lifecycle: development, fine-tuning, deployment, and optimization. Each stage matters, and the lessons learned at the end feed back into the beginning. Below is a practical, readable walkthrough of each stage and the practices that help models succeed in production. Development: Problem, Data, and Baselines Development starts with a clear problem statement and the right data. Define the business objective, determine what success looks like, and gather representative data. Data preparation often takes the most time: clean the data, handle missing values, engineer features, and split the dat...

In this post, I explain Introduction to Generative AI with Large Language Models, Key Concepts & Definitions, Underlying Models: Transformers & Beyond, Modeling andTraining Foundations, Sampling & Decoding for Generation Quality, Prompting Strategies for Generative AI (zero-shot, few-shot, chain-of-thought prompting, role prompting, and advanced prompt tactics), Scaling & Emergent Capabilities in Generation, Mitigating Hallucination & Ensuring Output Reliability -RAG and grounding, and Advanced Generation: Multimodality & Specialized Content. If you want my full Gen AI with LLMs document also including the following topics, you can use the Contact Form (in the right pane) or message me in LinkedIn:  Popular Generative LLMs & Frameworks (GPT-series, Claude, PaLM, Gemini, LLaMA), Efficiency & Deployment Optimization distillation, quantization, parameter-efficient tuning etc.), Ethics, Privacy & Governance, Generative AI Project Workflow (end-to-e...

In this post, I explain Confusion Matrix in detail. Learn Confusion Matrix Definition and Intuition, Claim Approval Example, Confusion Matrix Table Layout, Core Concepts Explained (TP, TN, FP, FN), Confusion Matrix Formulae, Derived Metrics from the Confusion Matrix (Precision, Recall, F1, Specificity), and Visualization and Code. If you want to additionally learn about the following confusion matrix topics or comment, you can do so on my original Confusion Matrix article on LinkedIn here . Thresholding, ROC and PR Curves, Imbalanced Data and the Accuracy Paradox, Multiclass and Multi-Label Confusion Matrices (Visualization and Interpretation), Cost-Sensitive Decisions: Cost Matrix, Business Tradeoffs, and Setting Operational Thresholds, Calibration, Confidence, and When to Trust Model Probabilities, Practical Tips and Troubleshooting (Data leakage, label noise, sampling effects) — confusion matrix tutorial, debugging checklist for AI Developers and AI QA Testers, Ethics, Fairness an...

In this post, I explain Introduction to RAG in LLMs (Large Language Models), RAG Concepts in LLMs, Retrieval Modules and Vector Embeddings, Indexing Strategies and Vector Databases, Document Ingestion and Preprocessing, RAG in LLM Python, RAG Frameworks (such as LangChain and LlamaIndex), Retrieve‑Then‑Generate vs Generate‑Then‑Retrieve, Prompt Engineering for RAG and Evaluation Metrics for RAG. You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers. If you want my complete Retrieval-Augmented Generation (RAG) Framework in LLMs document that additionally includes the following important topics, you can message me on LinkedIn : Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge. Question : What does RAG stand for in...

In this post, I explain LLMs in Python, Python Setup & Installation, Inference with Transformers, Calling ChatGPT API in Python, Python Local Deployment with Hugging Face Models, Prompt Engineering in Python and FineTuning & Custom Training (including LoRA). You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers. If you want my complete Introduction to LLMs in Python document that additionally includes the following important topics, you can message me on LinkedIn : Python Advanced Techniques (Streaming, Batching & Callbacks), Python Efficiency & #Optimization (quantization, distillation, and parameter‑efficient tuning), Integration & Deployment Workflows, LLMs in Python Best Practices & Troubleshooting, and consolidated Introduction to LLMs in Python Quiz (with answer explanations to reinforce learning). Question : What do I mean by "Introduction to LLMs in Python"? Answer : Introduction to LL...