Verwandte Themen
Der Kurs enthält
- 29 Std. 51 Min. Video
- 106 Lektionen
- Niveau: Fortgeschritten
- Sprache: English
- Wir merken uns, wo du warst
- Komplett kostenlos, ohne Karte
Lektionen
11 Abschnitte · 106 Lektionen · 29 Std. 51 Min. Gesamtlänge
Lektionen 1–1010 Lektionen · 1 Std. 55 Min.
- A Gentle Introduction to Machine Learning13 Min.
- Machine Learning Fundamentals: Cross Validation6 Min.
- Machine Learning Fundamentals: The Confusion Matrix7 Min.
- Machine Learning Fundamentals: Sensitivity and Specificity12 Min.
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1 Min.
- Machine Learning Fundamentals: Bias and Variance7 Min.
- Entropy (for data science) Clearly Explained!!!17 Min.
- Mutual Information, Clearly Explained!!!16 Min.
- The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9 Min.
- Linear Regression, Clearly Explained!!!27 Min.
Lektionen 11–2010 Lektionen · 2 Std. 12 Min.
- Multiple Regression, Clearly Explained!!!5 Min.
- Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12 Min.
- Design Matrices For Linear Models, Clearly Explained!!!15 Min.
- Odds and Log(Odds), Clearly Explained!!!12 Min.
- Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16 Min.
- StatQuest: Logistic Regression9 Min.
- Logistic Regression Details Pt1: Coefficients19 Min.
- Logistic Regression Details Pt 2: Maximum Likelihood10 Min.
- Logistic Regression Details Pt 3: R-squared and p-value15 Min.
- Saturated Models and Deviance19 Min.
Lektionen 21–3010 Lektionen · 2 Std. 18 Min.
- Logistic Regression in R, Clearly Explained!!!!17 Min.
- Deviance Residuals6 Min.
- ROC and AUC, Clearly Explained!16 Min.
- ROC and AUC in R15 Min.
- Regularization Part 1: Ridge (L2) Regression20 Min.
- Regularization Part 2: Lasso (L1) Regression8 Min.
- Ridge vs Lasso Regression, Visualized!!!9 Min.
- Regularization Part 3: Elastic Net Regression5 Min.
- Ridge, Lasso and Elastic-Net Regression in R18 Min.
- StatQuest: Principal Component Analysis (PCA), Step-by-Step22 Min.
Lektionen 31–4010 Lektionen · 1 Std. 32 Min.
- StatQuest: PCA main ideas in only 5 minutes!!!6 Min.
- StatQuest: PCA - Practical Tips8 Min.
- StatQuest: PCA in R9 Min.
- StatQuest: PCA in Python12 Min.
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15 Min.
- Bam!!! Clearly Explained!!!3 Min.
- StatQuest: MDS and PCoA8 Min.
- StatQuest: MDS and PCoA in R8 Min.
- StatQuest: t-SNE, Clearly Explained12 Min.
- StatQuest: Hierarchical Clustering11 Min.
Lektionen 41–5010 Lektionen · 2 Std. 6 Min.
- StatQuest: K-means clustering9 Min.
- Clustering with DBSCAN, Clearly Explained!!!10 Min.
- StatQuest: K-nearest neighbors, Clearly Explained6 Min.
- Naive Bayes, Clearly Explained!!!15 Min.
- Gaussian Naive Bayes, Clearly Explained!!!9 Min.
- Decision and Classification Trees, Clearly Explained!!!18 Min.
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5 Min.
- Regression Trees, Clearly Explained!!!23 Min.
- How to Prune Regression Trees, Clearly Explained!!!16 Min.
- One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15 Min.
Lektionen 51–6010 Lektionen · 3 Std. 39 Min.
- Classification Trees in Python from Start to Finish1 Std. 6 Min.
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating10 Min.
- StatQuest: Random Forests Part 2: Missing data and clustering11 Min.
- StatQuest: Random Forests in R15 Min.
- The Chain Rule, Clearly Explained!!!18 Min.
- Gradient Descent, Step-by-Step24 Min.
- Stochastic Gradient Descent, Clearly Explained!!!11 Min.
- AdaBoost, Clearly Explained21 Min.
- Gradient Boost Part 1 (of 4): Regression Main Ideas16 Min.
- Gradient Boost Part 2 (of 4): Regression Details27 Min.
Lektionen 61–7010 Lektionen · 4 Std. 4 Min.
- Gradient Boost Part 3 (of 4): Classification17 Min.
- Gradient Boost Part 4 (of 4): Classification Details37 Min.
- Troll 2, Clearly Explained!!!5 Min.
- XGBoost Part 1 (of 4): Regression26 Min.
- XGBoost Part 2 (of 4): Classification25 Min.
- XGBoost Part 3 (of 4): Mathematical Details27 Min.
- XGBoost Part 4 (of 4): Crazy Cool Optimizations24 Min.
- XGBoost in Python from Start to Finish57 Min.
- CatBoost Part 1: Ordered Target Encoding9 Min.
- CatBoost Part 2: Building and Using Trees16 Min.
Lektionen 71–8010 Lektionen · 2 Std. 56 Min.
- Cosine Similarity, Clearly Explained!!!10 Min.
- Support Vector Machines Part 1 (of 3): Main Ideas!!!21 Min.
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7 Min.
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16 Min.
- Support Vector Machines in Python from Start to Finish.45 Min.
- The Essential Main Ideas of Neural Networks19 Min.
- Neural Networks Pt. 2: Backpropagation Main Ideas18 Min.
- Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.19 Min.
- Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13 Min.
- Neural Networks Pt. 3: ReLU In Action!!!9 Min.
Lektionen 81–9010 Lektionen · 2 Std. 33 Min.
- Neural Networks Pt. 4: Multiple Inputs and Outputs14 Min.
- Neural Networks Part 5: ArgMax and SoftMax14 Min.
- The SoftMax Derivative, Step-by-Step!!!7 Min.
- Neural Networks Part 6: Cross Entropy10 Min.
- Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22 Min.
- Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15 Min.
- Recurrent Neural Networks (RNNs), Clearly Explained!!!17 Min.
- Long Short-Term Memory (LSTM), Clearly Explained21 Min.
- Word Embedding and Word2Vec, Clearly Explained!!!16 Min.
- Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!17 Min.
Lektionen 91–10010 Lektionen · 4 Std. 9 Min.
- Attention for Neural Networks, Clearly Explained!!!16 Min.
- Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36 Min.
- Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!37 Min.
- Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!19 Min.
- Tensors for Neural Networks, Clearly Explained!!!10 Min.
- Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30 Min.
- The matrix math behind transformer neural networks, one step at a time!!!24 Min.
- The StatQuest Introduction to PyTorch23 Min.
- Introduction to Coding Neural Networks with PyTorch and Lightning21 Min.
- Long Short-Term Memory with PyTorch + Lightning33 Min.
Lektionen 101–1066 Lektionen · 2 Std. 28 Min.
- Word Embedding in PyTorch + Lightning32 Min.
- Coding a ChatGPT Like Transformer From Scratch in PyTorch31 Min.
- Reinforcement Learning: Essential Concepts18 Min.
- Reinforcement Learning with Neural Networks: Essential Concepts24 Min.
- Reinforcement Learning with Neural Networks: Mathematical Details25 Min.
- Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18 Min.
Was du brauchst
- Erfahrung wird erwartet: der Kurs liegt auf fortgeschrittenem Niveau.
Beschreibung
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.
Für wen dieser Kurs ist
- Alle, die bereits mit Machine learning arbeiten.
Diese beiden Punkte sind unsere Einschätzung aus dem Niveau. Die Beschreibung stammt vom Autor.