A machine learning primer built from first principles. For engineers who want to reason about ML systems the way they reason about software systems.

Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

'Primer to Machine Learning' is a comprehensive guide covering essential topics in machine learning, including statistics, data preprocessing, supervised and unsupervised learning, neural networks, deep learning, NLP, time series analysis, and reinforcement learning. Perfect for beginners and intermediates.

A long term project for creation of a primer for statistical methods; prerequisite to Machine Learning.

Machine Learning Project which predict the discounts on the products avalaible on Amazon and Flipkart This is the capstone project of Summer Analytics, a primer course on Data Science, conducted by Consulting and Analytics Club of IIT Guwahati. Description Artificial Intelligence is an integral part of all major e-commerce companies today. Today's online retail platforms are heavily powered by algorithms and applications that use AI. Machine learning is used in a variety of ways, from inventory control and quality assurance in the warehouse to product recommendations and sales demographics on the website. Let’s say you want to create a promotional campaign for an e-commerce store and offer discounts to customers in the hopes that this might increase your sales. You have been provided descriptions of products on Amazon and Flipkart, including details like product title, ratings, reviews, and actual prices. In this challenge, you will predict discounted prices of the listed products based on their ratings and actual prices.

This challenge is the capstone project of the Summer Analytics, a primer course on Data Science, conducted by Consulting and Analytics Club of IIT Guwahati in the summers. The dataset is provided by DeltaX is the pioneering cross-channel digital advertising platform. The cloud-based platform leverages big data, user behavior, and machine learning algorithms to improve performance across the business funnel of advertisers. Problem Statement Let's take a case where an advertiser on the platform (DeltaX) would like to estimate the performance of their campaign in the future. Imagine it is the first day of March and you are given the past performance data of ads between 1st August to 28th Feb. You are now tasked to predict an ad's future performance (revenue) between March 1st and March 15th. Well, it is now time for you to put on your problem-solving hats and start playing with the data provided under the "data" section.

A practical machine learning tutorial for childhood obesity researchers. Companion to “A Primer for Machine Learning Applications in Childhood Obesity.”

This tutorial is a primer on image data related machine learning. You will learn the basics of image data preprocessing & data augmentation. Then you will build classification models to classify plant species using traditional ML algorithm, CNN-based and pre-trained models.

Machine Learning Primer is a gentle introduction to a huge topic like Machine Learning. It is for absolute beginners who know a little bit Python, have a knowledge of high school Algebra and Statistice.

Introduction to self-adapting methods. A primer artificial intelligence/machine learning algorithms.

A primer containing the prerequisite math, statistics, and probability to get started with data science and machine learning.

My notes for COMP30027 (Machine Learning), semester 1 2025. Topics include data types, primer to statistics, and different techniques for learning and classification.

Code from UG to PG. Including Algorithms 4th, C++ Primer 5th, Head First Java, Machine Learning, MATLAB, Nonlinear Optimization

[AI Sync] A machine learning primer built from first principles. For engineers who want to reason about ML systems the way they reason about software systems.

This repo houses my final project for a machine learning course at TEC de Costa Rica. Titled 'Forecasting Exchange Rates with Multivariate Neural Networks,' it showcases how to solve a forecasting problem for time series with recurrent neural networks. Although time-constrained, it offers a primer for more sophisticated models.

The Mechanics of Machine Learning Book contents Work in progress Book version 0.4 Terence Parr and Jeremy Howard Copyright © 2018-2019 Terence Parr. All rights reserved. Please don't replicate on web or redistribute in any way. This book generated from markup+markdown+python+latex source with Bookish. You can make comments or annotate this page by going to the annotated version of this page. You'll see existing annotated bits highlighted in yellow. They are PUBLICLY VISIBLE. Or, you can send comments, suggestions, or fixes directly to Terence. Warning: The content of this book is so unexciting that you'll be able to use it in your actual job! This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. We focus on just a few powerful models (algorithms) that are extremely effective on real problems, rather than presenting a broad survey of machine learning algorithms as many books do. Co-author Jeremy used these few models to become the #1 competitor for two consecutive years at Kaggle.com. This narrow approach leaves lots of room to cover the models, training, and testing in detail, with intuitive descriptions and full code implementations. This is a book in progress and we will add chapters and make edits

This notebook is a very basic and simple introductory primer to the method of ensembling models, in particular the variant of ensembling known as Stacking. In a nutshell stacking uses as a first-level (base), the predictions of a few basic machine learning models (classifiers) and then uses another model at the second-level to predict the output from the earlier first-level predictions. The Titanic dataset is a prime candidate for introducing this concept as many newcomers to Kaggle start out here. Furthermore even though stacking has been responsible for many a team winning Kaggle competitions there seems to be a dearth of kernels on this topic so I hope this notebook can fill somewhat of that void. I myself am quite a newcomer to the Kaggle scene as well and the first proper ensembling/stacking script that I managed to chance upon and study was one written in the AllState Severity Claims competition by the great Faron. The material in this notebook borrows heavily from Faron's script although ported to factor in ensembles of classifiers whilst his was ensembles of regressors. Anyway please check out his script here: Stacking Starter : by Faron Now onto the notebook at hand and I hope that it manages to do justice and convey the concept of ensembling in an intuitive and concise manner. My other standalone Kaggle script which implements exactly the same ensembling steps (albeit with different parameters) discussed below gives a Public LB score of 0.808 which is good enough to get to the top 9% and runs just under 4 minutes. Therefore I am pretty sure there is a lot of room to improve and add on to that script. Anyways please feel free to leave me any comments with regards to how I can improve