Automated Machine Learning Bootcamp: Build 15 Real Projects

自动化机器学习 (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非常耗时、资源密集且具有挑战性。它需要多个学科的专家，包括数据科学家——目前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，通过在原始数据上运行系统过程并选择从数据中提取最相关信息的模型，在现实世界中更容易构建和使用机器学习模型——这通常被称为“信号输入”。噪音。” 自动化机器学习结合了来自顶级数据科学家的机器学习最佳实践，使整个组织的数据科学更易于访问。

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——这对一家公司来说是很多要求，更不用说一位数据科学家了（前提是你可以雇佣和留住一个）。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并贬低您可能从模型中获得的见解。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自行开发功能，同时提高数据科学计划的投资回报率并减少获取价值所需的时间。

我们将涵盖您需要了解的关于世界顶级公司所需的完整数据科学和机器学习技术堆栈的所有信息。我们的学生已在戴尔、Google Developers、TCS、Wipro 和其他顶级科技公司找到工作！我们利用我们在线和面对面教学的经验来构建课程，以提供一种清晰、结构化的方法，引导您不仅了解如何使用数据科学和机器学习库，而且了解我们为什么使用它们。本课程在实际案例研究和机器学习算法背后的数学理论之间取得平衡。

Solve Data Science Problems Using Auto-ML, Learn To Use Eval ML, Pycaret, Auto Keras, Auto SK Learn, H20 Auto ML

New | Created by Pianalytix . | Last updated 10/2021

Duration: 10h 13m | 16 sections | 17 lectures | Video: 1280×720, 44 KHz | 4.4 GB
Genre: eLearning | Language: English + Sub

What you’ll learn
Make robust Machine Learning models
Understand the full product workflow for the machine learning lifecycle.
Have a great intuition of many Machine Learning models
Learn best practices when it comes to Data Science Workflow
Master Auto Machine Learning and use it on the job
Make powerful analysis
Learn best practices for real-world data sets

Requirements
Some prior coding or python scripting experience is required.
Basic Knowledge of machine learning & data science
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.

We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at Dell, Google Developers, TCS, Wipro, and other top tech companies! We’ve structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real-world case studies and the mathematical theory behind machine learning algorithms.

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.