Section 1.1: Virgilio

Virgilio is recognized as a revolutionary mentor in the realm of online data science education, striving to make learning accessible to everyone. This project offers a structured learning pathway to help students navigate their educational journey without feeling lost.

The curriculum is divided into three tiers to cater to various levels of expertise: Paradiso for theoretical insights, Purgatorio for foundational knowledge, and Inferno for advanced applications.

Learning begins at the Paradiso level, which focuses on theoretical foundations without delving into coding. Topics include:

• Understanding machine learning and its significance
• Exploring the necessity of machine learning
• Identifying use cases and teaching strategies

This level serves as an excellent starting point for individuals new to data science, helping them to grasp the fundamentals of the field.

Following Paradiso, learners transition to the Purgatorio level, where they will cover essential topics necessary for data science, including:

• Fundamental mathematics and statistics
• Basic programming in Python
• Problem definition and data exploration
• Machine learning training

With a structured approach, Purgatorio ensures that learners build their skills progressively, starting from the basics.

The final tier, Inferno, targets advanced users, providing specialized knowledge in areas such as:

• Time Series Analysis
• Computer Vision
• Natural Language Processing

This level also includes resources related to specific data science tools and libraries, with content continuously updated.

Virgilio is supported by a dedicated team of experts who contribute to its development. If you're interested, consider reaching out to the team to learn more or get involved.

Section 1.2: MLCourse

MLCourse, spearheaded by Yury Kashnitsky from OpenDataScience, is an open-source project aimed at enhancing machine learning education through a balanced mix of theory and practice. The courses are designed for individuals with a foundational understanding of data science, particularly in Python and mathematics, though beginners are also welcome to engage with the material.

The project encompasses ten structured topics, including:

• Exploratory Data Analysis with Pandas
• Visual Data Analysis
• Classification techniques such as Decision Trees and K-NN
• Ordinary Least Squares and Linear Models
• Bagging and Feature Engineering
• Unsupervised Learning
• Optimization strategies
• Time Series analysis
• Gradient Boosting

Each topic is equipped with an easy-to-follow guide, example notebooks, assignments, and video resources.

While MLCourse development ceased in 2019 for English content, the materials remain relevant and beneficial, particularly for those starting their data science journey.

Section 1.3: ProjectLearn

ProjectLearn is an open-source initiative that offers a curated selection of tutorial projects. The focus here is on hands-on learning, allowing participants to acquire specific skills rather than general knowledge.

While ProjectLearn encompasses a variety of fields, it includes a dedicated section for Machine Learning and AI, making it a valuable resource for those interested in these areas.

Most resources link to external articles or videos, but they are carefully curated to help learners explore practical applications of machine learning.

Section 1.4: Deepkapha

Deepkapha is an open-source platform that aggregates numerous tutorials on Artificial Intelligence and Deep Learning. This project is best suited for individuals with a basic understanding of data science and programming, making it an excellent choice for those ready to deepen their knowledge.

Deepkapha primarily focuses on Deep Learning and various frameworks, offering insights into concepts and differences among them. Additionally, it features a collection of blogs from various authors, providing an extensive resource for those interested in Deep Learning.

Section 1.5: Best-of ML Python

Best-of ML Python is part of the broader Best-of open-source initiative, which curates a daily selection of open-source packages and tools. This specific segment focuses on machine learning packages tailored for the Python programming language.

While it does not provide tutorials, Best-of ML Python categorizes an abundance of high-quality Python packages, making it easier for learners to discover resources relevant to their studies.