Machine Learning in Python – Extras

机器学习应用程序如今无处不在，从 Google Translate 和 NLP API，到 YouTube、Netflix 和 Amazon、Udemy 等使用的推荐系统。正如我们所知道的，数据科学和机器学习对于任何企业和部门的成功都非常重要——那么构建有效的机器学习系统需要什么？

在执行机器学习和数据科学项目时，正常的工作流程是您有一个要解决的问题，因此您执行数据收集、数据准备、特征工程、模型构建和评估，然后部署您的模型。然而，这还不是全部，这个生命周期还有很多。

在本课程中，我们将向您介绍一些大多数机器学习课程未涵盖的额外内容 – 例如使用管道，特别是 Scikit-learn 管道、Spark 管道等以及处理不平衡数据集等

我们还将探索 Scikit-learn、Tensorflow 或 Pytorch 之外的其他 ML 框架，例如用于在线机器学习的 TuriCreate、Creme 等。

我们将学习模型解释和解释。某些 ML 模型在生产中使用时往往存在偏差，因此在本课程中，我们将探讨如何检测模型的公平性和偏差。

在课程结束时，您将对整个机器学习项目生命周期中的额外概念和工具以及执行数据科学项目时要考虑的事项有一个全面的概述。

本课程没有脚本，有趣且令人兴奋，但同时我们深入探讨了机器学习生命周期的一些额外方面。

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 4.12 GB | Duration: 11h 20m
Explore ML Pipelines with Scikit-Learn,PySpark, Model Fairness and Model Interpretation, and More

Description
Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works?

In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle.

In this course we will be introducing to you some extra things that is not covered in most machine learning courses – such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etc

We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.

We will learn about model interpretation and explanation. Certain ML models when used in production tend to be bias, hence in this course we will explore how to detect model fairness and bias.

By the end of the course you will have a comprehensive overview of extra concepts and tools in the entire machine learning project life cycle and things to consider when performing a data science project.

This course is unscripted,fun and exciting but at the same time we dive deep into some extra aspects of the machine learning life cycle.

Specifically you will learn

Pipelines and their advantages.

How to build ML Pipelines with Scikit-Learn

How to build Spark NLP Pipelines

How to work with and fix Imbalanced Datasets

Model Fairness and Bias Detection

How to interpret and explain your Black Box Models using Lime,Eli5,etc

Incremental/Online Machine Learning Frameworks

Best practices in data science project

Model Deployment

Alternative ML Libraries eg TuriCreate,etc

how to track your ML experiments and more

etc

NB: This course will not cover CI/CD ML Pipelines

Join us as we explore the world of machine learning in python – the Extras