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
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Welcome to Machine Learning Fundamentals. Today we'll explore key concepts starting with variance. Variance measures how spread out data points are from the mean. It's calculated as the average of squared differences from the mean. Watch how the distribution changes as variance increases.
Unsupervised learning discovers hidden patterns without labeled data. K-means clustering groups similar data points together by finding cluster centers. Time series analysis examines sequential data over time, often modeling trends and patterns like seasonal variations or noise components.
Gradient descent is a fundamental optimization algorithm that finds the minimum of a cost function. It updates parameters by moving in the direction opposite to the gradient. Stochastic gradient descent uses random mini-batches instead of the full dataset, making it faster and often more effective for large datasets.
Linear regression models continuous relationships using a straight line, minimizing squared errors. Logistic regression uses the sigmoid function to output probabilities for classification tasks. Regularization techniques like L1 and L2 add penalty terms to prevent overfitting by constraining model complexity.