İlgili konular
Bu kursta neler var
- 29 sa 51 dk video
- 106 ders
- Seviye: İleri
- Dil: English
- Nerede kaldığını hatırlıyoruz
- Tamamen ücretsiz, kartsız
Dersler
11 bölüm · 106 ders · 29 sa 51 dk toplam süre
1–10. dersler10 ders · 1 sa 55 dk
- A Gentle Introduction to Machine Learning13 dk
- Machine Learning Fundamentals: Cross Validation6 dk
- Machine Learning Fundamentals: The Confusion Matrix7 dk
- Machine Learning Fundamentals: Sensitivity and Specificity12 dk
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1 dk
- Machine Learning Fundamentals: Bias and Variance7 dk
- Entropy (for data science) Clearly Explained!!!17 dk
- Mutual Information, Clearly Explained!!!16 dk
- The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9 dk
- Linear Regression, Clearly Explained!!!27 dk
11–20. dersler10 ders · 2 sa 12 dk
- Multiple Regression, Clearly Explained!!!5 dk
- Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12 dk
- Design Matrices For Linear Models, Clearly Explained!!!15 dk
- Odds and Log(Odds), Clearly Explained!!!12 dk
- Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16 dk
- StatQuest: Logistic Regression9 dk
- Logistic Regression Details Pt1: Coefficients19 dk
- Logistic Regression Details Pt 2: Maximum Likelihood10 dk
- Logistic Regression Details Pt 3: R-squared and p-value15 dk
- Saturated Models and Deviance19 dk
21–30. dersler10 ders · 2 sa 18 dk
- Logistic Regression in R, Clearly Explained!!!!17 dk
- Deviance Residuals6 dk
- ROC and AUC, Clearly Explained!16 dk
- ROC and AUC in R15 dk
- Regularization Part 1: Ridge (L2) Regression20 dk
- Regularization Part 2: Lasso (L1) Regression8 dk
- Ridge vs Lasso Regression, Visualized!!!9 dk
- Regularization Part 3: Elastic Net Regression5 dk
- Ridge, Lasso and Elastic-Net Regression in R18 dk
- StatQuest: Principal Component Analysis (PCA), Step-by-Step22 dk
31–40. dersler10 ders · 1 sa 32 dk
- StatQuest: PCA main ideas in only 5 minutes!!!6 dk
- StatQuest: PCA - Practical Tips8 dk
- StatQuest: PCA in R9 dk
- StatQuest: PCA in Python12 dk
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15 dk
- Bam!!! Clearly Explained!!!3 dk
- StatQuest: MDS and PCoA8 dk
- StatQuest: MDS and PCoA in R8 dk
- StatQuest: t-SNE, Clearly Explained12 dk
- StatQuest: Hierarchical Clustering11 dk
41–50. dersler10 ders · 2 sa 6 dk
- StatQuest: K-means clustering9 dk
- Clustering with DBSCAN, Clearly Explained!!!10 dk
- StatQuest: K-nearest neighbors, Clearly Explained6 dk
- Naive Bayes, Clearly Explained!!!15 dk
- Gaussian Naive Bayes, Clearly Explained!!!9 dk
- Decision and Classification Trees, Clearly Explained!!!18 dk
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5 dk
- Regression Trees, Clearly Explained!!!23 dk
- How to Prune Regression Trees, Clearly Explained!!!16 dk
- One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15 dk
51–60. dersler10 ders · 3 sa 39 dk
- Classification Trees in Python from Start to Finish1 sa 6 dk
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating10 dk
- StatQuest: Random Forests Part 2: Missing data and clustering11 dk
- StatQuest: Random Forests in R15 dk
- The Chain Rule, Clearly Explained!!!18 dk
- Gradient Descent, Step-by-Step24 dk
- Stochastic Gradient Descent, Clearly Explained!!!11 dk
- AdaBoost, Clearly Explained21 dk
- Gradient Boost Part 1 (of 4): Regression Main Ideas16 dk
- Gradient Boost Part 2 (of 4): Regression Details27 dk
61–70. dersler10 ders · 4 sa 4 dk
- Gradient Boost Part 3 (of 4): Classification17 dk
- Gradient Boost Part 4 (of 4): Classification Details37 dk
- Troll 2, Clearly Explained!!!5 dk
- XGBoost Part 1 (of 4): Regression26 dk
- XGBoost Part 2 (of 4): Classification25 dk
- XGBoost Part 3 (of 4): Mathematical Details27 dk
- XGBoost Part 4 (of 4): Crazy Cool Optimizations24 dk
- XGBoost in Python from Start to Finish57 dk
- CatBoost Part 1: Ordered Target Encoding9 dk
- CatBoost Part 2: Building and Using Trees16 dk
71–80. dersler10 ders · 2 sa 56 dk
- Cosine Similarity, Clearly Explained!!!10 dk
- Support Vector Machines Part 1 (of 3): Main Ideas!!!21 dk
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7 dk
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16 dk
- Support Vector Machines in Python from Start to Finish.45 dk
- The Essential Main Ideas of Neural Networks19 dk
- Neural Networks Pt. 2: Backpropagation Main Ideas18 dk
- Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.19 dk
- Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13 dk
- Neural Networks Pt. 3: ReLU In Action!!!9 dk
81–90. dersler10 ders · 2 sa 33 dk
- Neural Networks Pt. 4: Multiple Inputs and Outputs14 dk
- Neural Networks Part 5: ArgMax and SoftMax14 dk
- The SoftMax Derivative, Step-by-Step!!!7 dk
- Neural Networks Part 6: Cross Entropy10 dk
- Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22 dk
- Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15 dk
- Recurrent Neural Networks (RNNs), Clearly Explained!!!17 dk
- Long Short-Term Memory (LSTM), Clearly Explained21 dk
- Word Embedding and Word2Vec, Clearly Explained!!!16 dk
- Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!17 dk
91–100. dersler10 ders · 4 sa 9 dk
- Attention for Neural Networks, Clearly Explained!!!16 dk
- Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36 dk
- Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!37 dk
- Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!19 dk
- Tensors for Neural Networks, Clearly Explained!!!10 dk
- Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30 dk
- The matrix math behind transformer neural networks, one step at a time!!!24 dk
- The StatQuest Introduction to PyTorch23 dk
- Introduction to Coding Neural Networks with PyTorch and Lightning21 dk
- Long Short-Term Memory with PyTorch + Lightning33 dk
101–106. dersler6 ders · 2 sa 28 dk
- Word Embedding in PyTorch + Lightning32 dk
- Coding a ChatGPT Like Transformer From Scratch in PyTorch31 dk
- Reinforcement Learning: Essential Concepts18 dk
- Reinforcement Learning with Neural Networks: Essential Concepts24 dk
- Reinforcement Learning with Neural Networks: Mathematical Details25 dk
- Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18 dk
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Açıklama
Machine Learning covers a lot of topics and this can be intimidating. However, there is no reason to fear, this play list will help you trough it all, one step at a time.
Bu kurs kimin için
- Zaten Machine learning ile çalışanlar.
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