这周两篇文章:
1 机器学习是万能的吗?AI落地有哪些先决条件?如果你刚接触ML,或者对ML觉得很神秘,请先看下这篇文章。 2 如何做才能真正提升计算速度?硬件再牛,也难以招架业务场景中产生的数据,提高算法性能和计算速度是永远的话题。
最近有人问有没有相关数据集,这几天抽时间整理了以下数据集,标题即是Kaggle竞赛题目,可以直接搜索获得赛题详细介绍,在此列出10个参赛队伍最多的竞赛题及标签,最重要的是提供数据集的下载。
Kaggle是提升理解ML的较好平台,学的再多,都不如现在开始动手实践,简历上写的会再多算法,都不如有1个竞赛TOP3有说服力。
1 Titanic: Machine Learning from Disaster
Start here! Predict survival on the Titanic and get familiar with ML basics
2 House Prices-Advanced Regression Techniques
Predict sales prices practice feature engineering, RFs, and gradient boosting
3 Digit Recognizer
CV starts here! Learn computer vision fundamentals with the famous MNIST data
4 TalkingData AdTracking Fraud Detection Challenge
fraudulent click starts here! Can you detect fraudulent click traffic for mobile app ads?
5 Toxic Comment Classification Challenge
NLP starts here! Identify and classify toxic online comments
6 Santander Customer Satisfaction
HOT Which customers are happy customers?
7 2018 Data Science Bowl
CV Find the nuclei in divergent images to advance medical discovery
8 Bike Sharing Demand
Forecasting Forecast use of a city bikeshare system
9 Instacart Market Basket Analysis
选品分析 Which products will an Instacart consumer purchase again?
10 San Francisco Crime Classification
多分类预测 Predict the category of crimes that occurred in the city by the bay
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