Machine learning fundamentals

Machine Learning Fundamentals is a comprehensive course designed to introduce you to the core concepts, tools, and algorithms that form the foundation of machine learning. This course is ideal for those looking to build a solid understanding of machine learning, from the basics to more advanced techniques, and apply these skills using powerful tools like Apache Spark.

Starting with an introduction to machine learning, you’ll learn the fundamentals, including task formulation, data manipulation methods, and model evaluation. You’ll explore different types of ML algorithms and gain practical experience with clustering techniques. This foundational knowledge will set the stage for deeper exploration into machine learning applications.

The course also provides in-depth coverage of Spark MLLib and Spark ML, key tools for implementing machine learning at scale. You’ll learn how to build and manage ML pipelines and work with algorithms such as decision trees, Naïve Bayes, logistic regression, and neural networks. Additionally, the course offers a comprehensive look at clustering methods, including hierarchical clustering and Gaussian mixture models.

Each module of the course is balanced between theory and practice, ensuring that you not only understand the concepts but can also apply them in real-world scenarios. With equal emphasis on theoretical understanding and hands-on experience, this course prepares you to tackle machine learning challenges effectively.

Upon completing this course, participants will:

• Understand the fundamental concepts of machine learning and how to formulate ML tasks.
• Gain proficiency in using Spark MLLib and Spark ML to implement machine learning models.
• Develop and evaluate various ML models, including decision trees, Naïve Bayes, logistic regression, and neural networks.
• Apply clustering techniques, such as hierarchical clustering and Gaussian mixture models, to real-world data.

This course offers a balanced mix of theory and practice, providing you with a comprehensive understanding of machine learning concepts and the practical skills needed to apply them. Through hands-on exercises, you will learn how to work with machine learning tools and algorithms, making this course ideal for those looking to advance their knowledge and skills in machine learning.

Upon completion of the "Machine Learning Fundamentals" course, trainees will be able to:

1. Formulate and define machine learning tasks and select appropriate algorithms to solve them.
2. Utilize Spark MLLib and Spark ML to build, manage, and optimize machine learning models.
3. Implement and evaluate key machine learning algorithms such as decision trees, Naïve Bayes, logistic regression, and neural networks.
4. Apply clustering techniques, including hierarchical clustering and Gaussian mixture models, to analyze and segment data effectively.
5. Develop the practical skills necessary to implement machine learning solutions in real-world scenarios, ensuring both scalability and performance.

• [Theory – 2,5h: Practice – 1.5h] Introduction to Machine Learning

The fundamentals of ML

Tasks formulation

ML algorithms

Data manipulation methods

Model evaluation

Clustering algorithms

• [Theory – 2h: Practice – 2h] Spark MLLib and Spark ML

MLLIB

ML Pipelines

• [Theory – 1.5h: Practice – 2.5h] Spark MLLib and Spark ML

MLLIB

ML Pipelines

• [Theory – 1.5h: Practice – 2.5h] Algorithms

Decision Trees

Naïve Bayes

• [Theory – 1.5h: Practice – 2.5h] Algorithms

Logic Regression

Neural Nets

• [Theory – 3h: Practice – 1h] Clustering

Clustering basics

Hierarchical clustering

Gaussian mixture model

Hard EM

Stanford EM