Machine Learning Foundations

Machine Learning Foundations
https://WebToolTip.com
Published 8/2026

MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 1h 27m | Size: 1.61 GB
Learn core machine learning concepts, algorithms, evaluation methods, workflows, deployment, and model monitoring.
What you'll learn

Explain what machine learning is and how it differs from traditional programming and artificial intelligence.

Understand the differences between supervised, unsupervised, and other common machine learning approaches.

Identify practical machine learning use cases across business, technology, healthcare, finance, marketing, and operations.

Prepare datasets by cleaning data, handling missing values, transforming variables, and organizing features.

Understand the purpose of feature engineering and create useful inputs for machine learning models.

Divide data into training, validation, and testing sets to support reliable model development.

Understand how classification algorithms predict categories or labels.

Use regression techniques to predict continuous numerical values.

Apply clustering methods to discover patterns and groups within unlabeled data.

Select suitable algorithms based on the problem, data, and desired outcome.

Evaluate classification and regression models using appropriate performance metrics.

Use validation techniques to estimate how well a model will perform on unseen data.

Recognize overfitting and underfitting and apply techniques to improve model generalization.

Conduct error analysis to understand where and why a model produces incorrect results.

Describe the stages of a complete machine learning workflow, from data preparation to deployment.

Understand the basics of training pipelines, model deployment, monitoring, retraining, and maintenance.
Requirements

No previous machine learning or artificial intelligence experience is required.

The course is designed to be approachable for beginners.

Basic computer and internet-navigation skills are sufficient.

Familiarity with simple mathematics, percentages, averages, and charts can be helpful.

Basic Python knowledge is recommended for learners who want to complete coding exercises, but it is not required for understanding the main concepts.

A computer with an internet connection is recommended.

Access to a Python environment, code editor, or notebook platform may be helpful for practical experimentation.

No advanced calculus, statistics, or linear algebra background is required.

Previous experience working with spreadsheets or datasets can be useful but is not mandatory.

Curiosity about data, prediction, automation, and analytical problem-solving is the most important prerequisite.Beginners who want a clear introduction to machine learning concepts and workflows.