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Mammoth Club All levels 23 sections 109 lectures

Essential 10-Hour Machine Learning Foundations Video Bundle

Go beyond just writing machine learning models—understand how and why they work. This in-depth theory-focused collection is designed for aspiring ML engineers, data scientists, and researchers who want to master the principles, math, and logic behind modern machine learning algorithms.

01
Skill level
All levels
02
Sections
23
03
Lectures
109
04
Instructor
Team Mammoth
What's inside

This course includes.

23
Sections
8
Quizzes
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

23 sections · 109 lectures
+ 00a Foundations of AI App Empires 3 lectures
How billion-dollar apps apply ML to core features Preview Free preview
Understanding the data-to-insight lifecycle in ML Locked
Business models that scale with ML Locked
+ 00b AI-Powered Product Patterns 4 lectures
AI-Powered Product Patterns Locked
ML-powered feed ranking (TikTok, Instagram) Locked
Smart assistants & chat Locked
Submit a Question/Feedback Locked
+ 00c Machine Learning Theory Crash Course 12 lectures
Intro to Machine Learning Slides Locked
01 Course Intro Locked
02 Quick Intro To Machine Learning Locked
03 Deep Dive Into Machine Learning Locked
04 Problems Solved With Machine Learning Part 1 Locked
05 Problems Solved With Machine Learning Part 2 Locked
06 Types Of Machine Learning Locked
07 How Machine Learning Works Locked
08 Common Machine Learning Structures Locked
09 Steps To Build A Machine Learning Program Locked
10 Summary And Outro Locked
Fill in the Blank Quiz - Machine Learning Intro Locked
+ 01 Introduction to Machine Learning 12 lectures
01 What Is Machine Learning Preview Free preview
02 Types Of Machine Learning Models Locked
03 What Is Supervised Learning Locked
04 What Is Unsupervised Learning Locked
05 How Does A Machine Learning Agent Learn Locked
06 What Is Inductive Learning Locked
07a Performance Of A Machine Learning Algorithm Locked
07b Calculate Error Of Machine Learning Model Locked
08 Handle Noise In Data Locked
09 Powerful Tools With Machine Learning Libraries Locked
10 Gradient Descent Locked
11 Linear Algebra for Machine Learning Locked
+ 01a Regression Models 9 lectures
01 What Is Linear Regression Locked
02 What Is Logistic Regression Locked
03 Prepare Data For Logistic Regression Locked
04a How To Prepare Data Locked
04b How To Build A Logistic Regression Model Locked
04c What Is Optimization Locked
05 How To Optimize A Logistic Regression Model Locked
Quiz - Regression Locked
Source Files Locked
+ 01b Gradient Boosted Classification 5 lectures
01 What Is Gradient Boosting Locked
02 How To Shape Data For Classification Locked
03 How To Build A Boosted Trees Classifier Locked
Source Files Locked
Quiz - Gradient Boosted Classification Locked
+ 02a Deep Learning 3 lectures
01 What Is Deep Learning Locked
02 What Is A Neural Network Locked
Source Files Locked
+ 02b Convolutional Neural Network 5 lectures
01 What is a Convolutional Neural Network Locked
02a What Are Wrappers Locked
02b Source Files Locked
03 What is the Adam Optimizer Locked
Mindmap - Structure of a CNN Locked
+ 02c Layers of a CIFAR-Image Classification Neural Network 5 lectures
01 2D Convolution Layer Locked
02 Relu Activation Function Locked
03 2D Max Pooling Layer Locked
04 Flatten And Dense Layers Locked
Source Files Locked
+ 02d Optimizer and Loss for CIFAR-Image Classification Neural Network 3 lectures
01 How Do You Build An Optimizer For CIFAR-image Classification Locked
02 How Do You Calculate Loss For CIFAR-image Classification Locked
Source Files Locked
+ 03 Generative Neural Network Fundamentals 6 lectures
01 What Is A Generative Neural Network Locked
02 What Is A Convolutional Neural Network Locked
03 How To Build A Convolutional Neural Network Locked
04 How Do You Build A Generator Locked
Source Files Locked
Flashcards - Generative Neural Network Locked
+ 04 Discriminator Neural Network Fundamentals 2 lectures
01 How Do You Build A Discriminator Locked
Source Files Locked
+ 05 Other Types of Neural Networks 5 lectures
01 How Do You Build A Neural Network For Image Segmentation Locked
02 What Is A Recurrent Neural Network Locked
03 What Is A Dynamic Neural Network Locked
04 What Is A Bi-directional Neural Network Locked
Source Files Locked
+ 06 Word2Vec Sentiment Classification of Words 5 lectures
01 How Do You Build An Embedding Layer Locked
02 What Is Word2vec Locked
03 How Do You Vectorize Text Locked
04 How Do You Build A Word2vec Sequential Layer Locked
Source Files Locked
+ 07 Learn Image Generation Machine Learning Through Interview Questions 3 lectures
01 What Is Discriminative Modeling Locked
02 What Is Generative Modeling Locked
Interview Source Files Locked
+ 08a Diffusion Deep Learning Interview Questions 4 lectures
01 What Are Diffusion Models Locked
02 How Diffusion Works In Deep Learning Locked
03 Forward And Backward Diffusion In ML Locked
04 What Is A U-Net ML Model Locked
+ 08b Stable Diffusion Interview Questions 3 lectures
01 Steps Of Latent Reverse Diffusion In Stable Diffusion Locked
02 What Is Latent Space Locked
03 What Is The Manifold Hypothesis In ML Locked
+ 09 Dimensionality Reduction Data Science Interview Questions 2 lectures
01 What Is Dimensionality Reduction Locked
02 What Is Principal Component Analysis Locked
+ 10 Autoencoder Machine Learning Interview Questions 5 lectures
01 What Are Autoencoders Locked
02 What Are Encoders And Decoders In ML Locked
03 How Do Autoencoders Work Locked
04 What Are Variational Autoencoders Locked
05 What Is A Vector Quantized Variational Autoencoder Locked
+ 11 GAN Neural Network Interview Questions 3 lectures
01 What Is The Structure Of A Generative Adversarial Network Locked
02 What Are Discriminators And Generators Locked
03 What Is Zero-Shot Learning Locked
+ 12 Scoring Interview Questions 4 lectures
01 What Is Inception Score Locked
02 What Is Frechet Inception Distance Locked
03 How Fid Works In ML Locked
04 What Is Kernel Inception Distance Locked
+ 13 Signal Processing Interview Questions 4 lectures
01 What Is Time-Series Data Locked
02 What Is Signal Data Locked
03 Continuous Signals Vs Discrete Signals Locked
04 What Is Nyquist Rate Locked
+ 14 Fourier Analysis Interview Questions 2 lectures
01 What Are Periodic Signals Locked
02 What Is Fourier Transform Locked
Description

About this course.

✅ Learn the core theory behind supervised and unsupervised learning

✅ Understand bias-variance tradeoff, underfitting vs. overfitting, and error

✅ Dive into the mathematics of loss functions, optimization, gradient descent, and regularization

✅ Explore foundational models like linear regression, logistic regression, decision trees, SVMs, and more

✅ Get a structured introduction to the theory behind neural networks, activation functions, and backpropagation

✅ Explore the foundations of clustering, dimensionality reduction, and kernel methods

✅ Build theoretical intuition for model selection, cross-validation, and performance metrics


📦 Includes lifetime access, downloadable source files, evaluated coding challenges, and quizzes—all in one course.


If you want to master machine learning from the inside out—this theory collection is your essential guide. Enroll now and build the foundation that makes you a truly expert ML practitioner.

Instructors

Taught by people who ship.

Team Mammoth

Team Mammoth

Instructor

Produced by a team of Mammoth Club industry experts. Over 14 years, Mammoth Club has built a global student community in 190+ countries with 9+ million courses sold, releasing over 1,000+ courses and 5,000+ hours of video content.

Ready to start building?

Go beyond just writing machine learning models—understand how and why they work. This in-depth theory-focused collection is designed for aspiring ML engineers, data scientists, and researchers who want to master the principles, math, and logic behind modern machine learning algorithms.

Buy lifetime access →