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 0:14:57 ReLU Function (Rectified Linear Unit) 0:15:11 Mean Squared Error 0:15:22 Root Mean Square Error 0:15:35 R-squared 0:15:51 L1 and L2 Regularization 0:16:07 Learning Rate 0:16:36 Naive Bayes Classifier 0:16:48 Cost Function 0:17:00 Confusion Matrix 0:17:22 Precision 0:17:33 Recall 0:17:55 Area Under the Curve (AUC) 0:18:19 Train Test Split 0:18:40 Grid Search 0:19:17 Anomaly Detection 0:19:39 Missing Values 0:20:02 Euclidean Distance 0:20:19 Manhattan Distance 0:20:41 Hamming Distance 0:20:59 Jaccard Similarity 0:21:11 K-means Clustering 0:21:32 Bootstrapping 0:21:51 Hierarchical Clustering 0:22:04 Matrix Multiplication 0:22:22 Jacobian Matrix 0:22:37 Hessian Matrix 0:22:54 Measures of Central Tendency 0:23:20 Activation Function 0:23:34 Artificial Neural Network (ANN) 0:23:53 Perceptron 0:24:18 Convolutional Neural Network (CNN) 0:24:48 Recurrent Neural Network (RNN) 0:25:27 Long Short-Term Memory (LSTM) 0:25:52 Transformer Model 0:26:24 Padding 0:26:45 Pooling 0:27:01 Variational Autoencoder 0:27:26 Quantum Machine Learning and also include math equations for each concept and go depth for all these ml models and math