Machine Learning in Python - Extras

[mitsumi]

Genre: eLearning | MP4 | Video: h264, 1280x720 | 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

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