Language: English | Size: 1.08 GB | Duration: 2h 24m
Master the Essentials of Modern Machine Learning
What you'll learn
Explore the fundamentals of an end-to-end machine learning application.
Carry out basic data cleaning and pre-processing in Python with Polars.
Build a pipeline to train machine learning models.
Implement regression, ensemble, and gradient-boosted models
Deploy a machine learning model using MLFlow.
Requirements
Very basic Python programming knowledge.
Familiarity with running code in Jupyter notebooks.
Description
Machine learning (ML) and AI are the key drivers of innovation today. Understanding how these models work can help you apply ML techniques effectively.In this course, expert instructor Joram Mutenge shows you how to master machine learning essentials by leveraging Python and the high-performance Polars library for advanced data manipulation.You will build an end-to-end machine learning application to predict laptop prices. Building this ML application will help you gain hands-on experience in data exploration, data processing, model creation, model evaluation, model tuning, and model deployment with MLFlow.Learn from a Data Science PractionerJoram has a master's degree in Data Science from the University of Illinois Urbana-Champaign, and currently works in data at a manufacturing company building demand forecasting models. He has years of experience building and deploying machine learning models. In this course, he shares the lessons he has learned along the way.Making the most of this courseThe modules in this course build on top of each other. Learn by following the order in which these modules are presented. This will help you understand the material better. To further cement the understanding, type out the code and run it on your computer instead of passively watching. Finally, apply the knowledge learned to your own dataset.
