Machine Learning Fundamentals: From Linear Regression to Neural Networks
Build intelligent systems by understanding the mathematics, algorithms, and intuition behind modern ML
This course is generated on-demand — tailored to your learning style with podcasts, flashcards, case studies, and assessments.
Want to adjust the focus, depth, or number of modules? You can customize before generating.
30-Day Learning Guarantee — If the course doesn't meet your expectations, we'll refund you. No questions asked.
Course overview
What you'll learn
You'll learn machine learning fundamentals by building a strong foundation in both theory and practice. This course cuts through the hype to show you how algorithms actually work—from the linear algebra powering regression models to the gradient descent optimizing neural networks. You'll understand why certain algorithms excel at specific tasks, when to choose classification over regression, and how to evaluate whether your model is truly learning or just memorizing.
We start with supervised learning: regression and classification algorithms that form the backbone of most ML applications. You'll grasp the mathematical principles behind algorithms like linear regression, logistic regression, decision trees, and support vector machines. Then we move into unsupervised learning and clustering techniques that find hidden patterns in data. Finally, you'll explore neural networks and backpropagation, understanding how these building blocks enable deep learning.
By the end, you'll think like a machine learning engineer. You'll know how to frame business problems as ML tasks, select appropriate algorithms, tune hyperparameters, and diagnose common issues like overfitting and bias. This isn't about memorizing formulas—it's about developing the intuition to build systems that learn from data.
Course curriculum
8 modules, designed for mastery
How Machines Learn: Core Concepts and the Training Process
~60 minExplore what machine learning actually means, the difference between training and inference, and how loss functions guide the learning process.
Linear and Polynomial Regression: Predicting Continuous Values
~75 minMaster regression algorithms using gradient descent, understand the bias-variance tradeoff, and learn when linear models are sufficient versus when you need polynomial features.
Classification Algorithms: Logistic Regression and Decision Trees
~80 minLearn how to predict categories using probability-based models and tree-based approaches, plus understand precision, recall, and the confusion matrix.
Support Vector Machines and the Kernel Trick
~70 minDiscover how SVMs find optimal decision boundaries and how kernel functions enable classification in high-dimensional spaces without computing explicit transformations.
Unsupervised Learning: Clustering and Dimensionality Reduction
~65 minExplore K-means clustering, hierarchical clustering, and PCA for finding patterns in unlabeled data and reducing feature dimensions.
Neural Networks Architecture and Forward Propagation
~75 minUnderstand how neurons, layers, and activation functions work together to learn complex patterns through weighted connections.
Backpropagation and Training Deep Networks
~85 minLearn how neural networks update their weights using backpropagation, chain rule calculus, and optimization techniques like Adam and RMSprop.
Model Evaluation, Regularization, and Avoiding Overfitting
~70 minMaster cross-validation, regularization techniques like L1/L2 penalties, dropout, and practical strategies for building models that generalize well to new data.
Total estimated time: ~10 hours across 8 modules
Everything you need
Six learning formats, one complete experience
Every module delivers content across multiple formats — each chosen for a specific learning science reason.
AI-Generated Podcasts
Two voices — an expert and a curious learner — break down complex topics in engaging conversations. Listening activates different cognitive pathways than reading, deepening comprehension.
Structured Key Concepts
Clear, pedagogically-framed core knowledge organized for progressive understanding. Each concept builds on the last, creating a coherent mental model.
Real-World Case Studies
Applied examples from actual scenarios show how theory works in practice. Case-based learning bridges the gap between knowing a concept and using it.
Interactive Flashcards
Active recall — testing yourself — is proven to improve retention by 50%+ compared to passive review. Flashcards make retrieval practice effortless.
Quizzes & Assessments
Multiple-choice questions with detailed explanations test understanding and reveal knowledge gaps before you move on. Mastery-based progression ensures nothing is skipped.
Written Assignments
Writing forces deeper processing than multiple choice. Synthesize your learning by applying concepts to realistic scenarios, with instant AI-powered feedback on your analysis.
Built on learning science
Every format is here for a reason
Erudia courses combine five proven learning methods into one seamless experience — so knowledge sticks, not just passes through.
Spaced Exposure
Content revisited across multiple formats — audio, text, flashcards, quizzes — reinforces memory through varied repetition. Each encounter strengthens the neural pathway differently.
Retrieval Practice
Flashcards and assessments force active recall — proven to improve retention by 50%+ versus passive reading. Every quiz is a memory-strengthening event.
Synthesis Through Writing
Written assignments require deeper processing than multiple choice. When you explain a concept in your own words, you discover what you truly understand and what you don't.
Multi-Format Learning
Audio, reading, case studies, and interactive practice mirror how people naturally absorb complex information. Each format activates different cognitive pathways, building richer understanding.
Mastery-Based Progression
You can't skip ahead until you've demonstrated understanding. This isn't arbitrary — it's how lasting learning works. Each module builds on the foundations laid by the previous one.
What learners are saying
Real courses, real feedback
“I expected a surface-level overview, but the course actually got into altitude-specific soil biology, frost-resilient guild planting, and water management for mountain terrain. The case studies were specific enough that I could apply them to my own site. The podcast episodes were perfect for listening while working in the garden.”
Victoire Coustou Hibert
Passionate Gardener · High Altitude Permaculture in Switzerland
“I've read the book twice, so I was skeptical a course could add anything. It did. The module on counter-strategies completely changed how I think about defensive positioning, and the written assignments forced me to actually apply the laws to situations I'm dealing with at work — not just passively absorb them.”
Mauritz Burenius
Author of Never Piss Off HR · The 48 Laws of Power
“This covered territory I haven't seen in any other course — residual valuation models for streaming libraries, probabilistic forecasting for franchise IP, portfolio construction across film, TV, and gaming assets. The quizzes caught gaps in my understanding I didn't know I had. Genuinely useful for anyone working in media finance.”
Andrew Kotliar
Media & Entertainment Finance · Advanced Valuation and Portfolio Management of Media IP
Start learning today
This course is generated on-demand — built for you in approximately 20 minutes.
Want to adjust the focus, depth, or number of modules? You can customize before generating.
30-Day Learning Guarantee — If the course doesn't meet your expectations, we'll refund you. No questions asked.
Single course: €9 · Unlimited access: €19/month
Full course with podcasts, flashcards, case studies & AI-graded assessments
FAQ
Frequently asked questions
You'll benefit from basic calculus (derivatives) and linear algebra (matrix operations), but we explain the essential math as we go. If you remember high school algebra and are comfortable with functions and graphs, you can start here and pick up the mathematical concepts in context. We focus on intuition first, formulas second.
Machine learning is the broader field of algorithms that learn from data—including decision trees, SVMs, and linear regression. Deep learning is a subset that specifically uses neural networks with multiple layers. This course covers both: traditional ML algorithms (which are often more interpretable and require less data) and the neural network foundations that enable deep learning.
Yes. You'll understand how to frame problems, select algorithms, train models, and evaluate their performance. While production ML systems require additional engineering skills (data pipelines, deployment, monitoring), you'll have the conceptual and algorithmic foundation to build functional models and collaborate effectively with ML teams.
Yes — and often richer than traditional single-format courses. Every course is built from curated web sources and structured using proven pedagogical frameworks: spaced exposure, retrieval practice, and mastery-based progression. A supervisor agent reviews all generated content for accuracy, consistency, and depth before it reaches you. The multi-format approach — podcasts, case studies, flashcards, written assignments with AI feedback — creates a more complete learning experience than most human-created courses that rely on video lectures alone.
Each course is divided into modules that take approximately 45-90 minutes each, depending on topic complexity. You can work through them at your own pace — there are no deadlines. Most learners complete a full course within 1-3 weeks depending on depth and schedule.
Every course includes AI-generated two-voice podcasts, structured key concepts, real-world case studies, interactive flashcards, multiple-choice quizzes, and written assignments with AI-powered feedback. All content is generated specifically for your course topic.
Yes. Erudia is fully responsive and works on any device — phone, tablet, or desktop. Listen to podcasts on the go, review flashcards during a commute, or complete assignments on your laptop. Your progress syncs across all devices.
We offer a 30-day learning guarantee. If you complete a course and don't feel you've genuinely learned something new, we'll refund your purchase — no questions asked. We're that confident in the science behind every course.