Note. Resize UFS Disk

OS: FreeBSD.
Disk: GPT, UFS. 

Proxmox increase live disk of VM.

gpart commit vtbd1
gpart show vtbd1
gpart resize -i 1 vtbd1
gpart show vtbd1
df -h
growfs /dev/gpt/cloud-data
df -h

Codefinity. Mastered all the "Foundations of Machine Learning" specialization courses.

The track provides an introduction to the essential concepts and techniques in the field of machine learning. 

This comprehensive learning journey covers various crucial topics, including the utilization of scikit-learn library for machine learning initiation, the application of Linear Regression for predictive modeling, exploration of Classification methods for categorizing data, and the study of Clustering algorithms to discover inherent patterns within datasets. 

By engaging with this track, learners will acquire a solid understanding of fundamental machine learning principles, enabling them to build predictive and analytical models across diverse domains.

8 courses

Syllabus

1. ML Introduction with scikit-learn
   
2. Linear Regression with Python
3. Classification with Python
4. Identifying Spam Emails (Project)
5. Cluster Analysis
6. Clustering Demystified (Project)
7. Ensemble Learning
8. Logistic Regression Mastering (Project)
   

Foundations of Machine Learning

 

Codefinity. Project - Logistic Regression Mastering

Project Logistic Regression Mastering to track Foundations of Machine Learning.

In this project, we are going to understand the career tracks of Data Scientists.

Author: Edoardo Cantagallo

Logistic Regression Mastering

Codefinity. Course Ensemble Learning

Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks. 

Author: Ruslan Shudra

Let's summarize and highlight the main information covered in the course.

Ensemble learning in machine learning is a technique that combines the predictions of multiple individual models (learners) to produce a more robust and accurate prediction or classification. It leverages the principle that by aggregating the opinions of multiple models, you can often achieve better results than relying on a single model.
There are three commonly used techniques for creating ensembles: bagging, boosting, and stacking.

Ensemble Learning

 

Codefinity. Project - Clustering Demystified

Project Clustering Demystified to track Foundations of Machine Learning.

In this project, we are going to understand what a cluster is and how to use it in Python.

Author: Edoardo Cantagallo

Clustering Demystified

 

Codefinity. Course Cluster Analysis.

Course Classification with Python of track Foundations of Machine Learning.

Clustering is one of the fundamental machine learning techniques that allows you to solve many complex problems in real life: market segmentation, anomaly detection, dimensionality reduction, revealing hidden patterns, etc.

Author: Ruslan Shudra

Cluster Analysis

 

Codefinity. Project - Identifying Spam Emails

Project Identifying Spam Emails to track Foundations of Machine Learning.

In this project, we are going to classify spam emails according to their content.

Author: Edoardo Cantagallo

Identifying Spam Emails

Codefinity. Course Classification with Python.

Course Classification with Python of track Foundations of Machine Learning.

In machine learning, classification is used in predictive modeling to assign input data with a class label. 

• k-NN Classifier
• Logistic Regression
• Decision Tree 
• Random Forest
• Comparing Models
  

 Author: Volodymyr Romanovych, Sofiia Piustonen

Classification with Python

 

Codefinity. Course Linear Regression with Python.

Course Linear Regression with Python of track Foundations of Machine Learning.

Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.

Author: Volodymyr Romanovych

• Simple Linear Regression 
• Multiple Linear Regression
• Polynomial Regression
• Choosing The Best Model

Linear Regression with Python