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. 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

Codefinity. Mastered all the "Python Data Analysis and Visualization" specialization courses.

Track curriculum encompasses a collection of pivotal courses that provide foundational knowledge and skills essential for a successful journey in the field of data science. 

These courses encompass the comprehensive study of key concepts, tools, and methodologies integral to the realm of data analysis and modeling. 

By delving into courses centered around numpy, pandas, statistics, probability theory, as well as mathematics tailored for data analysis and modeling, learners are equipped with a well-rounded toolkit to seamlessly navigate the intricacies of data-driven exploration, manipulation, and inference.

6 courses 

Syllabus

1. Ultimate NumPy
2. Pandas First Steps
3. Advanced Techniques in pandas
4. Mathematics for Data Analysis and Modeling
5. Probability Theory Basics
6. Ultimate Visualization with Python
   

Python Data Analysis and Visualization

Codefinity. Ultimate NumPy.

Course Ultimate NumPy of track Python Data Analysis and Visualization.

Unlock the full potential of Python's most essential library for numerical computing, NumPy. 

This comprehensive course is designed to take you from a beginner's understanding to an advanced level of proficiency in NumPy. 

Whether you're a data scientist, engineer, researcher, or developer, mastering NumPy is essential for efficient data manipulation, scientific computing, and machine learning.

Author: Kyryl Sidak

Ultimate NumPy

 

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.

Codefinity. Successfully completed track - Python. Preparation for Data Science. Consists of 9 courses.

Track "Preparation for Data Science", curriculum encompasses a collection of pivotal courses that provide foundational knowledge and skills essential for a successful journey in the field of data science. 

These courses encompass the comprehensive study of key concepts, tools, and methodologies integral to the realm of data analysis and modeling.
By delving into courses centered around NumPy, Pandas, statistics, probability theory, as well as mathematics tailored for data analysis and modeling, learners are equipped with a well-rounded toolkit to seamlessly navigate the intricacies of data-driven exploration, manipulation, and inference. 

The curriculum's diverse content ensures a robust understanding of critical elements in data science, cultivating a solid base for individuals venturing into this dynamic and ever-evolving domain.

Preparation for Data Science