• views: 114
• date: 15 September 2024
• posted by: mitsumi

Machine Learning With Polars






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.
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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