Machine Learning with Data Lake Foundation on AWS
The Machine Learning with Data Lake Foundation on Amazon Web Services (AWS) solution integrates with a variety of AWS services to provide a fully functional data lake, with data submission, ingest processing, aggregation, analysis, and searching capabilities. This data lake is integrated with Amazon SageMaker, a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
This solution is supported by an AWS Quick Start and was developed in partnership with 47Lining, a Hitachi Vantara Company, an AWS Machine Learning Competency holder.
相關推薦
Machine Learning with Data Lake Foundation on AWS
The Machine Learning with Data Lake Foundation on Amazon Web Services (AWS) solution integrates with a variety of AWS services to provide a fully
Data Lake Foundation on AWS
This Quick Start deploys a data lake foundation that integrates various AWS Cloud services and components to help you migrate data to the AWS Clou
Informatica Data Lake Management on AWS
This Quick Start builds a data lake environment on the Amazon Web Services (AWS) Cloud by deploying the Informatica Data Lake Management solution
OReilly.Hands-On.Machine.Learning.with.Scikit-Learn.and.TensorFlow學習筆記彙總
其中用到的知識點我都記錄在部落格中了：https://blog.csdn.net/dss_dssssd 第一章知識點總結： supervised learning k-Nearest Neighbors Linear Regression
Hands-on Machine Learning with Scikit-Learn and TensorFlow（中文版）和深度學習原理與TensorFlow實踐-學習筆記
監督學習：新增標籤。學習的目標是求出輸入與輸出之間的關係函式y=f(x)。樸素貝葉斯、邏輯迴歸和神經網路等都屬於監督學習的方法。 監督學習主要解決兩類核心問題，即迴歸和分類。 迴歸和分類的區別在於強調一個是連續的，一個是離散的。 非監督學習：不新增標籤。學習目標是為了探索樣本資料之間是否
Machine Learning with Time Series Data
As with any data science problem, exploring the data is the most important process before stating a solution. The dataset collected had data on Chicago wea
Machine Learning with GPUs on vSphere
Performance of Machine Learning workloads using GPUs is by no means compromised when running on vSphere. In fact, you can often achieve better aggregate pe
[Machine Learning with Python] Data Preparation by Pandas and Scikit-Learn
In this article, we dicuss some main steps in data preparation. Drop Labels Firstly, we drop labels for train set. Here we use drop() method in Pandas li
[Machine Learning with Python] My First Data Preprocessing Pipeline with Titanic Dataset
The Dataset was acquired from https://www.kaggle.com/c/titanic For data preprocessing, I firstly defined three transformers: DataFrameSelector: S
二、《Hands-On Machine Learning with Scikit-Learn and TensorFlow》一個完整的機器學習專案
本章中，你會假裝作為被一家地產公司剛剛僱傭的資料科學家，完整地學習一個案例專案。 下面是主要步驟： 1. 專案概述。 2. 獲取資料。 3. 發現並可視化資料，發現規律。 4. 為機器學習演算法準備資料。 5. 選擇模型，進行訓練。 6. 微調模型。 7. 給出解決方案。 8. 部
《Hands-On Machine Learning with Scikit-Learn & TensorFlow》讀書筆記 第一章 機器學習概覽
一、機器學習概覽 為什麼使用機器學習？ 機器學習善於： 需要進行大量手工調整或需要擁有長串規則才能解決的問題：機器學習演算法通常可以簡化程式碼、提高效能。 問題複雜，傳統方法難以解決：最好的機器學習方法可以找到解決方案。 環境有波動：機器學習演算法可以適
Hands on Machine Learning with Sklearn and TensorFlow學習筆記——機器學習概覽
一、什麼是機器學習？ 計算機程式利用經驗E（訓練資料）學習任務T（要做什麼，即目標），效能是P（效能指標），如果針對任務T的效能P隨著經驗E不斷增長，成為機器學習。【這是湯姆米切爾在1997年定義】 大白話：類比於學生學習考試，你先練習一套有一套的模擬卷 （這就相當於訓練資料），在這幾
Large-Scale Machine Learning with Spark on Amazon EMR
This is a guest post by Jeff Smith, Data Engineer at Intent Media. Intent Media, in their own words: “Intent Media operates a platform for adverti
Qubole on Data Lake Foundation
You are responsible for the cost of the AWS services used while running this Quick Start reference deployment. The AWS CloudFormation templat
《Hands-On Machine Learning with Scikit-Learn & TensorFlow》讀書筆記 第六章 決策樹
第六章 決策樹 CHAPTER 6 Decision Trees 和支援向量機一樣， 決策樹是一種多功能機器學習演算法， 即可以執行分類任務也可以執行迴歸任務， 甚至包括多輸出（multioutput）任務. 決策樹也是隨機森林的基本組成部分，而隨機
《Hands-On Machine Learning with Scikit-Learn & TensorFlow》讀書筆記 第五章 支援向量機
第5章 支援向量機 支援向量機（SVM）是個非常強大並且有多種功能的機器學習模型，能夠做線性或者非線性的分類，迴歸，甚至異常值檢測。機器學習領域中最為流行的模型之一，是任何學習機器學習的人必備的工具。SVM 特別適合應用於複雜但中小規模資料集的分類問題。
Introduction to Machine Learning with Python/Python機器學習基礎教程_程式碼修改與更新
2.3.1樣本資料集 --程式碼bug及修改意見 import matplotlib.pyplot as plt import mglearn X,y=mglearn.datasets.make_forge() mglearn.discrete_scatter(X[:,0
機器學習（Machine Learning and Data Mining）CS 5751——Lab1作業記錄
Activity3 繪製散點圖矩陣，顯示屬性之間的相關性： mpg，hp，disp，drat，wt，qsec。 使用散點圖，評論哪些屬性對具有最高的相關性。 plot(mtcars$wt, mtcars$mpg, main="WT vs. MPG", xla
【Machine Learning with Peppa】分享機器學習，數學，統計和程式設計乾貨
專欄達人 授予成功建立個人部落格專欄
Machine Learning with Peppa
把Scala List的幾種常見方法梳理彙總如下，日常開發場景基本上夠用了。建立列表scala> val days = List("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Sat