Essential Machine Learning and AI Concepts Animated
Learn about all the most important concepts and terms related to machine learning and AI.
Course developed by https://www.youtube.com/@turingtimemachine
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp
⭐️ Contents ⭐️
0:00:00 Introduction
0:00:31 Variance
0:00:58 Unsupervised Learning
0:01:11 Time Series Analysis
0:01:26 Transfer Learning
0:01:41 Gradient Descent
0:01:59 Stochastic Gradient Descent
0:02:12 Sentiment Analysis
0:02:24 Regression
0:02:33 Regularization
0:02:45 Logistic Regression
0:03:01 Linear Regression
0:03:20 Reinforcement Learning
0:03:33 Decision Trees
0:03:47 Random Forest
0:04:03 Truncation
0:04:16 Principal Component Analysis (PCA)
0:04:29 Pre-training
0:04:39 Object Detection
0:04:58 Oversampling
0:05:16 Outlier
0:05:28 Overfitting
0:05:44 One-Hot Encoding
0:05:57 Nearest Neighbor Search
0:06:09 Normal Distribution
0:06:18 Normalization
0:06:35 Natural Language Processing (NLP)
0:06:46 Matrix Factorization
0:06:58 Markov Chain
0:07:23 Model Selection
0:07:33 Model Evaluation
0:07:42 Jupyter Notebook
0:07:54 Knowledge Transfer
0:08:03 Knowledge Graphs
0:08:18 Joint Probability
0:08:28 Inductive Bias
0:08:41 Information Extraction
0:08:49 Inference
0:09:05 Imbalanced Data
0:09:15 Human in the Loop
0:09:30 Graphics Processing Unit (GPU)
0:09:41 Vanishing Gradient
0:09:55 Generalization
0:10:04 Generative Adversarial Networks (GANs)
0:10:19 Ensemble Methods
0:10:27 Multiclass Classification
0:10:38 Data Pre-processing
0:10:49 Regression Analysis
0:11:02 Sigmoid Function
0:11:13 Evolutionary Algorithms
0:11:24 Language Models
0:11:34 Backpropagation
0:11:46 Bagging
0:12:05 Dense Vector
0:12:19 Feature Engineering
0:12:29 Support Vector Machines (SVMs)
0:12:44 Cross-validation
0:13:15 Loss Function
0:13:29 P-value
0:13:47 T-test
0:13:57 Cosine Similarity
0:14:10 Dropout
0:14:21 Softmax Function
0:14:34 Bayes' Theorem
0:14:46 Tanh Function
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React: Production Server Setup Part 2 - Live Coding with Jesse
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cookies vs localStorage vs sessionStorage - Beau teaches JavaScript
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Browser history tutorial - Beau teaches JavaScript
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Graph Data Structure Intro (inc. adjacency list, adjacency matrix, incidence matrix)
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React: Parameterized Routing with Next.js - Live Coding with Jesse
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React: Dealing with jQuery Issues - Live Coding with Jesse
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setInterval and setTimeout: timing events - Beau teaches JavaScript
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Browser and Device Testing - Live Coding with Jesse
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Last Minute Updates - Live Coding with Jesse
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Post Launch Updates - Live Coding with Jesse
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React: Setting Up Google Analytics - Live Coding with Jesse
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React: Masonry Layout - Live Coding with Jesse
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Load Balancing Digital Ocean Droplets - Live Coding with Jesse
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try, catch, finally, throw - error handling in JavaScript
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Load Balancing: SSL Passthrough Setup - Live Coding with Jesse
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Graphs: breadth-first search - Beau teaches JavaScript
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React: WordPress API Live Search - Live Coding with Jesse
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Creating WordPress Custom Post Types - Live Coding With Jesse
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Dates - Beau teaches JavaScript
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Miscellaneous Front End Updates - Live Coding with Jesse
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Merging a Pull Request from GitHub - Live Coding with Jesse
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React + Prettier + Standard JS - Live Coding with Jesse
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React: Sortable Responsive Table - Live Coding with Jesse
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Geolocation Sorting by Distance - Live Coding with Jesse
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Tradeoff Matrix - Agile Software Development
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The Definition of Ready - Agile Software Development
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Getting first React job without experience - Ask Preethi
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React: Google Analytics Click Tracking - Live Coding with Jesse
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Submitting a PR to an Open Source Project - Live Coding with Jesse
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Should I go back to school to get CS degree? - Ask Preethi
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Hero Section CSS Changes - Live Coding with Jesse
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Working Agreement - Agile Software Development
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A day at Pennybox with Co-Founder Reji Eapen
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React: Sorting and Filtering Data - Live Coding with Jesse
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React: Sorting and Filtering Data Part 2 - Live Coding with Jesse
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React: Building a New UI - Live Coding with Jesse
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Definition of Done - Agile Software Development
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Getting started with jQuery (tutorial) - Beau teaches JavaScript
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Making a React Blog with WordPress Content - Live Coding with Jesse
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React, NextJS, CSS - Live Coding with Jesse
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jQuery events - Beau teaches JavaScript
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React/NextJS Routing and WordPress API Custom Types - Live Coding with Jesse
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React: Working with API Data - Live Coding with Jesse
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React: Refactoring Components - Live Streaming with Jesse
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jQuery effects - Beau teaches JavaScript
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More React Refactoring - Live Coding with Jesse
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animate in jQuery - Beau teaches JavaScript
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"Finishing" My React Site - Live Coding with Jesse
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Starting a New React Project (P2D1) - Live Coding with Jesse
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React Project 2 Day 2: Learning Material UI - Live Coding with Jesse
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The Agile Manifesto - Agile Software Development
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jQuery: get and set with http, text, val, and attr - Beau teaches JavaScript
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React Project 2 Day 3 - Live Coding with Jesse
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The INVEST approach to product backlog items
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React Project 2 Day 4 - Live Coding with Jesse
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Chickens and Pigs - Agile Software Development
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React Project 2 Day 5 - Live Coding with Jesse
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jQuery: add and remove DOM elements - Beau teaches JavaScript
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React Project 2 Day 6 - Live Coding with Jesse
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Chapters (65)
Introduction
0:31
Variance
0:58
Unsupervised Learning
1:11
Time Series Analysis
1:26
Transfer Learning
1:41
Gradient Descent
1:59
Stochastic Gradient Descent
2:12
Sentiment Analysis
2:24
Regression
2:33
Regularization
2:45
Logistic Regression
3:01
Linear Regression
3:20
Reinforcement Learning
3:33
Decision Trees
3:47
Random Forest
4:03
Truncation
4:16
Principal Component Analysis (PCA)
4:29
Pre-training
4:39
Object Detection
4:58
Oversampling
5:16
Outlier
5:28
Overfitting
5:44
One-Hot Encoding
5:57
Nearest Neighbor Search
6:09
Normal Distribution
6:18
Normalization
6:35
Natural Language Processing (NLP)
6:46
Matrix Factorization
6:58
Markov Chain
7:23
Model Selection
7:33
Model Evaluation
7:42
Jupyter Notebook
7:54
Knowledge Transfer
8:03
Knowledge Graphs
8:18
Joint Probability
8:28
Inductive Bias
8:41
Information Extraction
8:49
Inference
9:05
Imbalanced Data
9:15
Human in the Loop
9:30
Graphics Processing Unit (GPU)
9:41
Vanishing Gradient
9:55
Generalization
10:04
Generative Adversarial Networks (GANs)
10:19
Ensemble Methods
10:27
Multiclass Classification
10:38
Data Pre-processing
10:49
Regression Analysis
11:02
Sigmoid Function
11:13
Evolutionary Algorithms
11:24
Language Models
11:34
Backpropagation
11:46
Bagging
12:05
Dense Vector
12:19
Feature Engineering
12:29
Support Vector Machines (SVMs)
12:44
Cross-validation
13:15
Loss Function
13:29
P-value
13:47
T-test
13:57
Cosine Similarity
14:10
Dropout
14:21
Softmax Function
14:34
Bayes' Theorem
14:46
Tanh Function
🎓
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