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A Gentle Introduction to Machine Learning
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Machine Learning Fundamentals: Cross Validation
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Machine Learning Fundamentals: The Confusion Matrix
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Machine Learning Fundamentals: Sensitivity and Specificity
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The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!
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Machine Learning Fundamentals: Bias and Variance
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Entropy (for data science) Clearly Explained!!!
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Mutual Information, Clearly Explained!!!
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The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
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Linear Regression, Clearly Explained!!!
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Multiple Regression, Clearly Explained!!!
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Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
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Design Matrices For Linear Models, Clearly Explained!!!
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Odds and Log(Odds), Clearly Explained!!!
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Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
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StatQuest: Logistic Regression
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Logistic Regression Details Pt1: Coefficients
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Logistic Regression Details Pt 2: Maximum Likelihood
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Logistic Regression Details Pt 3: R-squared and p-value
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Saturated Models and Deviance
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Logistic Regression in R, Clearly Explained!!!!
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Deviance Residuals
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ROC and AUC, Clearly Explained!
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ROC and AUC in R
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Regularization Part 1: Ridge (L2) Regression
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Regularization Part 2: Lasso (L1) Regression
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Ridge vs Lasso Regression, Visualized!!!
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Regularization Part 3: Elastic Net Regression
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Ridge, Lasso and Elastic-Net Regression in R
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StatQuest: Principal Component Analysis (PCA), Step-by-Step
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StatQuest: PCA main ideas in only 5 minutes!!!
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StatQuest: PCA - Practical Tips
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StatQuest: PCA in R
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StatQuest: PCA in Python
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StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
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Bam!!! Clearly Explained!!!
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StatQuest: MDS and PCoA
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StatQuest: MDS and PCoA in R
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StatQuest: t-SNE, Clearly Explained
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StatQuest: Hierarchical Clustering
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StatQuest: K-means clustering
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Clustering with DBSCAN, Clearly Explained!!!
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StatQuest: K-nearest neighbors, Clearly Explained
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Naive Bayes, Clearly Explained!!!
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Gaussian Naive Bayes, Clearly Explained!!!
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Decision and Classification Trees, Clearly Explained!!!
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StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
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Regression Trees, Clearly Explained!!!
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How to Prune Regression Trees, Clearly Explained!!!
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One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
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Classification Trees in Python from Start to Finish
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StatQuest: Random Forests Part 1 - Building, Using and Evaluating
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StatQuest: Random Forests Part 2: Missing data and clustering
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StatQuest: Random Forests in R
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The Chain Rule, Clearly Explained!!!
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Gradient Descent, Step-by-Step
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Stochastic Gradient Descent, Clearly Explained!!!
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AdaBoost, Clearly Explained
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Gradient Boost Part 1 (of 4): Regression Main Ideas
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Gradient Boost Part 2 (of 4): Regression Details
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Gradient Boost Part 3 (of 4): Classification
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Gradient Boost Part 4 (of 4): Classification Details
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Troll 2, Clearly Explained!!!
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XGBoost Part 1 (of 4): Regression
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XGBoost Part 2 (of 4): Classification
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XGBoost Part 3 (of 4): Mathematical Details
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XGBoost Part 4 (of 4): Crazy Cool Optimizations
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XGBoost in Python from Start to Finish
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CatBoost Part 1: Ordered Target Encoding
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CatBoost Part 2: Building and Using Trees
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Cosine Similarity, Clearly Explained!!!
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Support Vector Machines Part 1 (of 3): Main Ideas!!!
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Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
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Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
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Support Vector Machines in Python from Start to Finish.
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The Essential Main Ideas of Neural Networks
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Neural Networks Pt. 2: Backpropagation Main Ideas
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Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
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Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
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Neural Networks Pt. 3: ReLU In Action!!!
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Neural Networks Pt. 4: Multiple Inputs and Outputs
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Neural Networks Part 5: ArgMax and SoftMax
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The SoftMax Derivative, Step-by-Step!!!
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Neural Networks Part 6: Cross Entropy
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Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
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Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs), Clearly Explained!!!
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Long Short-Term Memory (LSTM), Clearly Explained
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Word Embedding and Word2Vec, Clearly Explained!!!
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Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
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Attention for Neural Networks, Clearly Explained!!!
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Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
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Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
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Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
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Tensors for Neural Networks, Clearly Explained!!!
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Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
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The matrix math behind transformer neural networks, one step at a time!!!
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The StatQuest Introduction to PyTorch
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Introduction to Coding Neural Networks with PyTorch and Lightning
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Long Short-Term Memory with PyTorch + Lightning
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Word Embedding in PyTorch + Lightning
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Coding a ChatGPT Like Transformer From Scratch in PyTorch
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Reinforcement Learning: Essential Concepts
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Reinforcement Learning with Neural Networks: Essential Concepts
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Reinforcement Learning with Neural Networks: Mathematical Details
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Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!