ML Explained | Poke

ML Explained @UCzW1gIPo6ZXlQOvjkZsTarw@youtube.com

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ML concepts explained. For Data Science Interview questions

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AI Learns To Navigate: Q-Learning Basics

How AI Learns To Make Decisions |Q-Learning Explained | Beginner's Guide to Reinforcement Learning

From Certain to Uncertain | Stochastic Bellman Equation Made Easy

This Equation Powers AI Decision Making| Reinforcement Learning Basics

Reinforcement Learning Made Easy - Part 1

GPT-4o API Guide - Unlock the power of OpenAI's best model!

OpenELM vs Phi-3-mini - Apple vs Microsoft: Who reigns supreme in the battle of small LLMS?

Phi-3-mini vs Llama 3 Showdown: Testing the real world applications of small LLMs

LLaMA 3 vs Mistral vs GPT-4:RAG Test Blowout - Which Model Truly Reigns Supreme?

Master ChromaDB: Supercharge Your RAG Tasks with the Ultimate Vector Database!

Create RAG Using Ollama And Langchain - Chat with your documents

Create Your Custom Loss Function - Catboost

Building An Image Classifier Using GPT-4 Vision API

A Guide on How To Use GPT-4 Vision API | System Prompt and User Prompt Explained

Pandas Tricks That You Must Know and Start Using

How a False Positive Almost Ended The World

Build a Machine Learning Labelling Tool Using Gradio in 3 minutes

Deploy Your Machine Learning (ML) Model on HuggingFace Spaces using Gradio

What is MLaaS (Machine Learning as a Service) ?

LLMS Explained - Temperature, The parameter to control randomness in Language Models

LLMs Explained - Gorilla - A LLM to write API calls

Build a Machine Learning Demo Using Python and Gradio

Create a ChatBot in Python Using Llama2 and LangChain - Ask Questions About Your Own Data

Fine Tuning Llama2 (LLM) with custom data on Google Collab using LoRA

Create Your Own Loss Metric - Custom Evaluation Metrics in Catboost

How to find the Top K Elements in your data - Numpy Argpartition, an alternative to argmax #numpy

Matthews Correlation Coefficient (MCC Score) | A balanced ML metric to focus on both tp and tn

ML Metrics Every Data Scientist needs to know | Top N Accuracy

XgBoost Parameters that every Data Scientist must know #machinelearning

Leave One Out Encoding - How to Encode Categorical Variable for Boosting Models like XgBoost

Pandas Essentials | Apply

Pandas Essentials - Transform and Qcut (Quantile cut)

Calculate Correlation Among Categorical Variables in Python

Time Series Forecasting with Python Part -2 | Moving Averages

Pandas Essentials – Pivot, Pivot Table, Cast and Melt

Time Series Forecasting with Python - Part1

Categorical Encoding, Weight of Evidence encoding to win Kaggle competitions

F-beta score explained | F1-score, F0.5 score and F2 and where and when you should use it.

All about decision trees, calculating Gini, Information Gain, building a Decision Tree from scratch

Macro vs micro averages Explained for multi-class Classification | Machine Learning