Machine Learning

This document covers the complete AI application development journey, from ideation and model selection through building with RAG and fine-tuning to production deployment with MLOps best practices.

This document explores the multi-model approach for AI implementation covering model selection criteria, prompt engineering, continuous evaluation and collaborative team strategies for optimal AI deployment.

This document explores advanced prompt engineering methods including zero-shot, few-shot, chain-of-thought, and self-consistency techniques, along with practical tools and applications for effective LLM interactions.

This document introduces LangChain, an open-source Python framework for developing LLM applications, exploring its benefits, practical uses, and integration capabilities with various data types.

This document introduces in-context learning and prompt engineering explaining how LLMs can learn new tasks from examples provided in prompts without additional training, along with techniques for crafting effective prompts to guide AI systems.

This document introduces natural language processing, explaining how computers translate between unstructured human language and structured data through techniques like tokenization, stemming, lemmatization, part of speech tagging and named entity recognition.

This document explores foundation models and large language models, covering their training methodology, advantages in performance and productivity, as well as challenges related to compute costs and trustworthiness in enterprise applications.

This document introduces generative AI, its evolution from discriminative AI and the foundational models that enable creative content generation across text, images, video, and code.

This document introduces neural networks, their structure, types, and training process. It explains how neural networks are inspired by the human brain and highlights their applications in pattern recognition, image analysis, and sequential data processing.

This document provides an overview of deep learning, its key concepts applications in various fields, and the different types of models used. It also covers the training process and recent advancements in the field.

Explains the relationship between AI, machine learning, deep learning foundation models, generative AI, and large language models. Covers definitions, distinctions, and the evolution of foundational AI technologies.

Comprehensive guide on handling missing values and outliers in datasets including detection, imputation, and removal techniques with practical Python examples

Importance of data cleaning in machine learning, common issues with messy data, and methods for handling duplicate data to ensure reliable model outcomes

Methods for retrieving data from SQL and NoSQL databases, APIs, and Cloud data sources with practical considerations and Python code examples

Methods for retrieving data from various sources including CSV and JSON files with practical considerations using Python and Pandas

Foundational concepts, workflow, and vocabulary of machine learning providing clear understanding of tools and processes for building and deploying ML models

Current advancements in artificial intelligence highlighting transformative impacts across industries and practical applications shaping everyday life

Comprehensive overview of artificial intelligence history from its origins in the 1940s through modern developments and future possibilities

Overview of Machine Learning and Deep Learning, including their definitions differences, applications, and the importance of features and targets in ML projects

An overview of Artificial Intelligence and Machine Learning, their history applications, and impact.