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0:10:19 Ensemble Methods
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Classroom Contents
Essential Machine Learning and AI Concepts Animated
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- 1 0:00:00 Introduction
- 2 0:00:31 Variance
- 3 0:00:58 Unsupervised Learning
- 4 0:01:11 Time Series Analysis
- 5 0:01:26 Transfer Learning
- 6 0:01:41 Gradient Descent
- 7 0:01:59 Stochastic Gradient Descent
- 8 0:02:12 Sentiment Analysis
- 9 0:02:24 Regression
- 10 0:02:33 Regularization
- 11 0:02:45 Logistic Regression
- 12 0:03:01 Linear Regression
- 13 0:03:20 Reinforcement Learning
- 14 0:03:33 Decision Trees
- 15 0:03:47 Random Forest
- 16 0:04:03 Truncation
- 17 0:04:16 Principal Component Analysis PCA
- 18 0:04:29 Pre-training
- 19 0:04:39 Object Detection
- 20 0:04:58 Oversampling
- 21 0:05:16 Outlier
- 22 0:05:28 Overfitting
- 23 0:05:44 One-Hot Encoding
- 24 0:05:57 Nearest Neighbor Search
- 25 0:06:09 Normal Distribution
- 26 0:06:18 Normalization
- 27 0:06:35 Natural Language Processing NLP
- 28 0:06:46 Matrix Factorization
- 29 0:06:58 Markov Chain
- 30 0:07:23 Model Selection
- 31 0:07:33 Model Evaluation
- 32 0:07:42 Jupyter Notebook
- 33 0:07:54 Knowledge Transfer
- 34 0:08:03 Knowledge Graphs
- 35 0:08:18 Joint Probability
- 36 0:08:28 Inductive Bias
- 37 0:08:41 Information Extraction
- 38 0:08:49 Inference
- 39 0:09:05 Imbalanced Data
- 40 0:09:15 Human in the Loop
- 41 0:09:30 Graphics Processing Unit GPU
- 42 0:09:41 Vanishing Gradient
- 43 0:09:55 Generalization
- 44 0:10:04 Generative Adversarial Networks GANs
- 45 0:10:19 Ensemble Methods
- 46 0:10:27 Multiclass Classification
- 47 0:10:38 Data Pre-processing
- 48 0:10:49 Regression Analysis
- 49 0:11:02 Sigmoid Function
- 50 0:11:13 Evolutionary Algorithms
- 51 0:11:24 Language Models
- 52 0:11:34 Backpropagation
- 53 0:11:46 Bagging
- 54 0:12:05 Dense Vector
- 55 0:12:19 Feature Engineering
- 56 0:12:29 Support Vector Machines SVMs
- 57 0:12:44 Cross-validation
- 58 0:13:15 Loss Function
- 59 0:13:29 P-value
- 60 0:13:47 T-test
- 61 0:13:57 Cosine Similarity
- 62 0:14:10 Dropout
- 63 0:14:21 Softmax Function
- 64 0:14:34 Bayes' Theorem
- 65 0:14:46 Tanh Function
- 66 0:14:57 ReLU Function Rectified Linear Unit
- 67 0:15:11 Mean Squared Error
- 68 0:15:22 Root Mean Square Error
- 69 0:15:35 R-squared
- 70 0:15:51 L1 and L2 Regularization
- 71 0:16:07 Learning Rate
- 72 0:16:36 Naive Bayes Classifier
- 73 0:16:48 Cost Function
- 74 0:17:00 Confusion Matrix
- 75 0:17:22 Precision
- 76 0:17:33 Recall
- 77 0:17:55 Area Under the Curve AUC
- 78 0:18:19 Train Test Split
- 79 0:18:40 Grid Search
- 80 0:19:17 Anomaly Detection
- 81 0:19:39 Missing Values
- 82 0:20:02 Euclidean Distance
- 83 0:20:19 Manhattan Distance
- 84 0:20:41 Hamming Distance
- 85 0:20:59 Jaccard Similarity
- 86 0:21:11 K-means Clustering
- 87 0:21:32 Bootstrapping
- 88 0:21:51 Hierarchical Clustering
- 89 0:22:04 Matrix Multiplication
- 90 0:22:22 Jacobian Matrix
- 91 0:22:37 Hessian Matrix
- 92 0:22:54 Measures of Central Tendency
- 93 0:23:20 Activation Function
- 94 0:23:34 Artificial Neural Network ANN
- 95 0:23:53 Perceptron
- 96 0:24:18 Convolutional Neural Network CNN
- 97 0:24:48 Recurrent Neural Network RNN
- 98 0:25:27 Long Short-Term Memory LSTM
- 99 0:25:52 Transformer Model
- 100 0:26:24 Padding
- 101 0:26:45 Pooling
- 102 0:27:01 Variational Autoencoder
- 103 0:27:26 Quantum Machine Learning
