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Catalog / All levels / Complete Machine Learning Theory Collection with 10 Practice Exams
Mammoth Club All levels 23 sections 101 lectures

Complete Machine Learning Theory Collection with 10 Practice Exams

Created by Team Mammoth
·
101 lectures
01
Skill level
All levels
02
Sections
23
03
Lectures
101
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
23
Sections
✓
3
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

23 sections · 101 lectures
+ Welcome! 1 lecture Preview
Submit a Question / Feedback Locked
+ 00 Machine Learning Theory Crash Course 12 lectures
Intro to Machine Learning Slides Locked
00. Course Intro Locked
01. Quick Intro To Machine Learning Locked
02. Deep Dive Into Machine Learning Locked
03. Problems Solved With Machine Learning Part 1 Locked
04. Problems Solved With Machine Learning Part 2 Locked
05. Types Of Machine Learning Locked
06. How Machine Learning Works Locked
07. Common Machine Learning Structures Locked
08. Steps To Build A Machine Learning Program Locked
09. 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 Locked
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
07 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
00 What Is Linear Regression Locked
01 What Is Logistic Regression Locked
02 Prepare Data For Logistic Regression Locked
03 How To Prepare Data Locked
04a How To Build A Logistic Regression Model Locked
04b What Is Optimization Locked
05a How To Optimize A Logistic Regression Model Locked
Source Files Locked
Quiz - Regression 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
02 What Are Wrappers Locked
03 What is the Adam Optimizer Locked
02bSource Files 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
02c 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
02d Source Files Locked
+ 03 Generative Neural Network Fundamentals 5 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
Flashcards - Generative Neural Network Locked
+ 04 Discriminator Neural Network Fundamentals 1 lecture
01 How Do You Build A Discriminator Locked
+ 05 Other Types of Neural Networks 4 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
+ 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
+ 08 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
+ 08a 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
+ Where to find your exam 1 lecture
Find Your Exams Locked
Description

About this course.

Unlock the full potential of Machine Learning with this comprehensive course. Covering everything from basic concepts to advanced techniques, you'll learn about types of models, data scaling, error handling, and optimization methods like gradient descent. Master key algorithms, including K-Nearest Neighbors, Linear and Logistic Regression, Neural Networks, CNNs, RNNs, and more.

Explore applications like Natural Language Processing, sentiment analysis, and text vectorization, while also diving into cutting-edge technologies like GANs and Autoencoders. With 10 practice exams, you'll reinforce your learning and gain hands-on experience to confidently tackle real-world challenges.

Key Features:

Full coverage of Machine Learning theory and models
10 practice exams to test your knowledge
Focus on real-world applications like NLP and GANs
Master essential techniques for building ML solutions

Get ready to enhance your skills and take your career to the next level with Machine Learning! 

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?

Join Mammoth Club and move through the course with structured lessons, previews, and certificates.

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