A beginner-friendly introduction to the principles of data science and machine learning may be found in Microsoft’s Intro to Machine Learning and Data Science study materials. Data analysis, the machine learning lifecycle, supervised and unsupervised learning, model training, evaluation, and responsible AI are among the key topics covered in the [...]

SuperDataScience’s Machine Learning A-ZTM: AI, Python & R is a practical machine learning course that introduces students to the entire machine learning workflow, including data preprocessing, supervised and unsupervised learning, reinforcement learning, model evaluation, and real-world Python and R implementation. To help students practice machine learning concepts with real-world examples, [...]

This online course teaches students how to analyze data using a machine learning technique and introduces them to the principles of machine learning. Using Python and scikit-learn, it covers important topics like supervised and unsupervised learning, Naive Bayes, Support Vector Machines (SVM), Decision Trees, Linear Regression, Clustering, Feature Selection, PCA, [...]

For programmers who wish to learn how to create real-world machine learning models, Introduction to Machine Learning for Coders is a useful, hands-on machine learning course. Important subjects like data preparation, Random Forests, model validation, feature importance, neural networks, and developing data-driven applications are covered in the course. It is [...]

Johns Hopkins University’s practical machine learning course teaches students how to create and assess machine learning prediction models using actual data. Training and test datasets, cross-validation, overfitting, feature engineering, preprocessing, and machine learning techniques including regression, classification trees, Naive Bayes, and random forests are all covered in the course. Additionally, [...]

The University of London’s Machine Learning for All is a beginner-friendly course that introduces students to the basic ideas of artificial intelligence (AI) and machine learning without having any prior knowledge of mathematics or programming. Students investigate how machines learn from data, comprehend how data affects AI systems, look at [...]

Simplilearn’s Machine Learning Basics (Machine Learning using Python) is a free online course that introduces students to the principles of artificial intelligence and machine learning. Important topics covered in the course include supervised and unsupervised learning, logistic and linear regression, K-Means clustering, Support Vector Machines (SVM), Naive Bayes, time series [...]

The fundamental ideas of machine learning are introduced in Great Learning’s beginner-friendly online course, Introduction to Machine Learning. Supervised and unsupervised learning, linear regression, classification, recommendation systems, machine learning processes, and real-world case studies are all covered in the course. Additionally, it helps students develop a solid foundation in AI [...]

The goal of the beginner-friendly course AWS Machine Learning Foundations is to acquaint students with the principles of machine learning and its practical uses with AWS technology. Supervised and unsupervised learning, model creation, computer vision, reinforcement learning, and generative AI are among the key machine learning principles covered in the [...]

Harvard University’s intermediate-level course CS50, Introduction to Artificial Intelligence with Python, exposes students to the basic ideas and techniques behind contemporary artificial intelligence. Students investigate subjects including graph search algorithms, knowledge representation, probability, machine learning, neural networks, reinforcement learning, and natural language processing through practical Python projects. The course aids [...]