自动机器学习大师班:15个项目

Automated Machine Learning Masterclass: 15 (AutoML) Projects
使用自动ML解决数据科学问题，学会使用评估ML，Pycaret，Auto Keras，Auto SK Learn，H20自动ML

你会学到:
掌握Python上的自动机器学习，制作健壮的机器学习模型
构建数据科学和机器学习简历项目的现代组合。
对许多机器学习模型有很强的直觉
了解数据科学工作流的最佳实践
学习预处理数据、清理数据和分析大数据
学习预处理数据、清理数据和分析大数据。
创建有监督的机器学习算法来预测类。

时长:10h 15m |视频:. MP4，1280×720 30 fps |音频:AAC，44.1 kHz，2ch |大小解压后:4.53 GB 含课程项目文件
语言：英语+中英文字幕（云桥网络 机译）


要求:
机器学习知识

描述:
自动机器学习(AutoML)代表了各种规模的组织处理机器学习和数据科学的方式的根本转变。将传统的机器学习方法应用于现实世界的业务问题是耗时、资源密集型且具有挑战性的。它需要几个学科的专家，包括数据科学家——一些目前在就业市场上最受欢迎的专业人士。

自动化机器学习改变了这一点，通过对原始数据运行系统化的过程，并选择从数据中提取最相关信息的模型，使在现实世界中构建和使用机器学习模型变得更加容易——这通常被称为“噪音中的信号”。自动机器学习结合了顶级数据科学家的机器学习最佳实践，使数据科学在整个组织中更容易获得。

手动构建机器学习模型是一个多步骤的过程，需要领域知识、数学专业知识和计算机科学技能——这对一家公司来说要求很高，更不用说一名数据科学家了(前提是你能雇佣并保留一名)。不仅如此，还有无数人为错误和偏见的机会，这会降低模型的准确性，降低您可能从模型中获得的洞察力。自动化机器学习使组织能够使用数据科学家的成熟知识，而无需花费时间和金钱来开发自己的能力，同时提高数据科学计划的投资回报，并减少获取价值所需的时间。

一个数据科学家在美国挣多少钱？
2021年7月15日更新的2.8k薪酬报告显示，美国数据科学家的全国平均薪酬为每年1，20718美元(来源:glassdoor)
按公司、角色、平均基本工资列出的薪资(美元)
脸书数据科学家年收入136，000美元。从1014份薪水分析。
亚马逊数据科学家年收入1，25704美元。从307份薪水分析。
苹果数据科学家的年薪为1，53885美元。从147份薪水分析。
谷歌数据科学家年收入1，48316美元。从252份薪水分析。
IBM数据科学家的年薪为1，32662美元。从388份薪水分析。
微软数据科学家年薪1，338，10美元。从205份薪水分析。
英特尔公司数据科学家的年薪为1，259，30美元。从131份薪水分析。


在本课程中，我们将构建下面列出的15个真实世界的自动ML项目:
使用评估模型预测项目1心脏病发作风险
项目-2使用Pycaret检测信用卡欺诈
项目-3使用自动SK学习(回归)预测航班票价
项目-4使用Auto Keras预测汽油价格
基于H2O汽车ML的项目-5银行客户流失预测
使用端到端部署的TPOT的项目6空气质量指数预测器
项目7使用最大似然模型和端到端部署的PyCaret进行降雨预测
项目-8使用最大似然法和最大似然法(自动最大似然法)预测比萨饼价格
使用TPOT预测板球得分
项目-10使用ML和H2O汽车ML预测自行车租赁数量
使用Auto Keras预测项目-11混凝土抗压强度
项目-12班加罗尔房价使用自动SK学习
项目-13使用PyCaret预测医院死亡率
项目-14员工晋升评估使用ML和评估自动ML
利用最大似然法和H2O自动最大似然法预测饮用水可饮用性

唯一有15个自动语言项目的课程
(阅读此文):本课程值得您花费时间和金钱，请在优惠到期前立即注册。

这门课是给谁上的:
数据科学的初学者。


Duration: 10h 15m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 4.38 GB
Genre: eLearning | Language: English
Solve Data Science Problems Using Auto ML, Learn To Use Eval ML, Pycaret, Auto Keras, Auto SK Learn, H20 Auto ML

What you’ll learn:
Master Auto Machine Learning on Python, Make robust Machine Learning models
Construct a modern portfolio of data science and machine learning resume projects.
Have a great intuition of many Machine Learning models
Learn best practices when it comes to Data Science Workflow
Learn to pre process data, clean data, and analyze large data
Learn to pre process data, clean data, and analyze large data.
Create supervised machine learning algorithms to predict classes.

Requirements:
Knowledge Of Machine Learning

Description:
Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills – which is a lot to ask of one company, let alone one data scientist (provided you can hire and retain one). Not only that, there are countless opportunities for human error and bias, which degrades model accuracy and devalues the insights you might get from the model. Automated machine learning enables organizations to use the baked-in knowledge of data scientists without expending time and money to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.
How much does a Data Scientist make in the United States?
The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)
Salaries by Company, Role, Average Base Salary in (USD)
Facebook Data Scientist makes US$1,36,000/yr. Analyzed from 1,014 salaries.
Amazon Data Scientist makes US$1,25,704/yr. Analyzed from 307 salaries.
Apple Data Scientist makes US$1,53,885/yr. Analyzed from 147 salaries.
Google Data Scientist makes US$1,48,316/yr. Analyzed from 252 salaries.
IBM Data Scientist makes US$1,32,662/yr. Analyzed from 388 salaries.
Microsoft Data Scientist makes US$1,33,810/yr. Analyzed from 205 salaries.
Intel Corporation Data Scientist makes US$1,25,930/yr. Analyzed from 131 salaries.

In This Course, We Are Going To Build 15 Real World Auto-ML Projects Listed Below:
Project-1 Heart Attack Risk Prediction using Eval ML
Project-2 Credit Card Fraud Detection using Pycaret
Project-3 Flight Fare Prediction using Auto SK Learn(Regression)
Project-4 Petrol Price Forecasting using Auto Keras
Project-5 Bank Customer Churn Prediction using H2O Auto ML
Project-6 Air Quality Index Predictor using TPOT with End-To-End Deployment
Project-7 Rain Prediction using ML models and PyCaret with End-To-End Deployment
Project-8 Pizza Price Prediction using ML and EVALML(Auto ML)
Project-9 IPL Cricket Score prediction using TPOT
Project-10 Predicting Bike Rentals Count using ML and H2O Auto ML
Project-11 Concrete Compressive Strength Prediction using Auto Keras
Project-12 Bangalore House Price using Auto SK Learn
Project-13 Hospital Mortality Prediction using PyCaret
Project-14 Employee Evaluation for Promotion using ML and Eval Auto ML
Project-15 Drinking Water Potability Prediction using ML and H2O Auto ML

The Only Course With 15 Auto-ML Projects
(Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.

Who this course is for:
Anyone who is beginner in data science.
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