Foundations of Machine Learning

ML Fundamentals

From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.

ML with Python: A Practical Introduction

Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.

Machine Learning

Offers a wider understanding and practical skills for excelling at machine learning and pursuing research opportunities.

Unsupervised Algorithms in ML

The course covers the fundamentals of unsupervised learning methods and their real-world applications, particularly recommender systems.

ML with PySpark: Customer Churn Analysis

Develop a machine learning model using PySpark to forecast customer attrition and acquire practical experience in AI-driven business solutions.

Machine Learning Essentials

This course teaches Python-based machine learning techniques, including linear regression and classification.

ML with Spark on Google Cloud Dataproc

The purpose of this course is to provide students with the opportunity to develop practical, cloud-based machine learning skills. It focuses on the use of Apache Spark to teach logistic regression modeling on Google Cloud.

ML: Concepts & Applications

Learn to use Python and libraries for data tasks, understand key machine learning techniques, and apply them to real-world datasets for a strong research foundation.

ML: an overview

The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.

ML Regression

Master regression by predicting house prices, investigate regularized linear regression, manage extensive feature sets, and employ optimization algorithms to make precise predictions with large datasets.