Machine Learning With Polars - Printable Version

Master the Essentials of Modern Machine Learning

[b]What you'll learn[/b]

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.

[b]Requirements[/b]

Very basic Python programming knowledge.

Familiarity with running code in Jupyter notebooks.

[b]Description[/b]

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.

Overview

Section 1: Introduction

Lecture 1 A brief Introduction to Machine Learning

Section 2: Reading the Data

Lecture 2 Loading data

Section 3: Exploratory Data Analysis (EDA)

Lecture 3 Descriptive statistics and plots

Section 4: Cleaning and Processing

Lecture 4 Cleaning columns: Ram, Weight

Lecture 5 Cleaning column: Memory

Lecture 6 Cleaning column: Memory (part II)

Lecture 7 Cleaning column: Screen Resolution

Lecture 8 Cleaning column: CPU

Lecture 9 Cleaning column: GPU

Lecture 10 Cleaning column: Operating System

Lecture 11 Creating column: Clock Speed

Lecture 12 Selecting columns to use

Section 5: Data Transformation

Lecture 13 Standardizing numeric values

Lecture 14 One-Hot-Encoding categorical columns

Lecture 15 Data partitioning

Section 6: Model Building

Lecture 16 Model building: Dummy Regressor

Lecture 17 Model building: Linear Regression

Lecture 18 Model building: Decision Tree

Lecture 19 Model building: Catboost

Lecture 20 Model building: Random Forest

Section 7: Model Evaluation

Lecture 21 Model Evaluation: R-squared

Lecture 22 Model Evaluation: MSE

Lecture 23 Model Evaluation: MAE

Lecture 24 Model Evaluation: Residual plot

Section 8: Hyperparameter Tuning

Lecture 25 Hyperparameter tuning: Regression

Lecture 26 Hyperparameter tuning: Decision Tree

Lecture 27 Hyperparameter tuning: Catboost

Lecture 28 Hyperparameter tuning: GridSearchCV

Section 9: Model Deployment

Lecture 29 End-to-End Notebook

Lecture 30 Model deployment: MLFlow

Professionals with tabular data in spreadsheets or databases seeking to make predictions from it.,Students interested in learning the fundamentals of applied machine learning.,Students and professionals seeking to learn the implementation of regression, ensemble, and gradient-boosted models.,Data professionals interested in learning how to deploy a model into production.

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