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

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 Visualization with Python

Course Ultimate Visualization with Python of track Python Data Analysis and Visualization.

Data is everywhere around us, and making sense of it is extremely important. 

Visualization helps us deal with data by finding certain patterns and insights in it. 

We will develop a solid foundation of data visualization using Python and its libraries, such as matplotlib and seaborn, to get as much information from data as possible in a neat and concise way. Without further ado, let's dive in!

Author: Kyryl Sidak

 

Ultimate Visualization with Python

 

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.

Codefinty. Project - Recognizing Handwritten Digits.

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

• Recognizing Handwritten Digits.
  In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.
  

Recognizing Handwritten Digits

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