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. 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. 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. ML Introduction with scikit-learn.

Course. ML Introduction with scikit-learn of track Foundations of Machine Learning.

Machine Learning is now used everywhere. Want to learn it yourself? 

This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project. 

This course is intended for students with a basic knowledge of Python, Pandas, and NumPy.

Author: Volodymyr Romanovych

 

ML Introduction with scikit-learn.