Machine Learning in Python - Extras

Explore ML Pipelines with Scikit-Learn,PySpark, Model Fairness and Model Interpretation, and More


What you'll learn
Machine Learning
Working with Imbalanced Datasets
Working with Pipelines
Model Interpretation and Explainable AI
Model Bias and Fairness Checking
Online Machine Learning Tools

Requirements
Willingness to Learn
Basic understanding of Machine Learning Concepts and Python

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
Who this course is for:
Python Developers and ML Enthusiasts
Individuals curious about Data Science and Machine Learning

More Info