Essential Machine Learning and AI Concepts Animated

freeCodeCamp.org · Beginner ·📐 ML Fundamentals ·1y ago
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 0
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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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