Beginner Machine Learning Pipelines Learning Path

Google’s AI Course for Beginners (in 10 minutes)!

Learn How I'd learn ML in 2025 (if I could start over)

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Clear and simple explanations of machine learning algorithms. Understand the math and intuition behind ML with Josh Starmer.

Google's fast-paced, practical introduction to machine learning. A self-study guide for aspiring machine learning practitioners.

The original Stanford ML course taught by Andrew Ng

An in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts.

This specialization teaches how to use machine learning for tasks like demand forecasting and predicting product usage. It covers Python libraries for data manipulation and dives into advanced AI techniques like neural networks and random forests for supply chain challenges.

This specialization covers the essential mathematical foundations for machine learning, including linear algebra, multivariate calculus, and principal component analysis (PCA). It's designed to provide the necessary mathematical background for a career in ML.

Imperial College London Mathematics for Machine Learning: Linear Algebra

Part of the DeepLearning.AI specialization, this course teaches the core concepts of linear algebra and how they are applied in machine learning and data science.

This course introduces simple and multiple linear regression models, allowing you to assess the relationship between variables in a data set and a continuous response variable.

This course is a non-technical introduction to the basics of machine learning, including supervised learning concepts.

The third course in the DeepLearning.AI specialization, focusing on the fundamentals of probability and statistics for machine learning and data science.

An IBM-led course that covers a variety of machine learning algorithms, including a section on decision trees and ensemble methods with hands-on labs.

An intermediate-level course that introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers basic statistics of data sets, such as mean values and variances and the computation of distances and angles between vectors using inner products.

A focused course on logistic regression and other supervised machine learning techniques using Python.

The fifth course in the Google Advanced Data Analytics Certificate. You'll practice modeling variable relationships using methods such as linear regression, ANOVA, and logistic regression.

This is a 3-course specialization that provides a broad introduction to modern machine learning. It covers supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for AI and machine learning innovation.