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Что входит в курс
- 29 ч 51 мин видео
- 106 уроков
- Уровень: Продвинутый
- Язык: English
- Помним, где вы остановились
- Полностью бесплатно, без карты
Уроки
11 разделов · 106 уроков · 29 ч 51 мин общая длительность
Уроки 1–1010 уроков · 1 ч 55 мин
- A Gentle Introduction to Machine Learning13 мин
- Machine Learning Fundamentals: Cross Validation6 мин
- Machine Learning Fundamentals: The Confusion Matrix7 мин
- Machine Learning Fundamentals: Sensitivity and Specificity12 мин
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1 мин
- Machine Learning Fundamentals: Bias and Variance7 мин
- Entropy (for data science) Clearly Explained!!!17 мин
- Mutual Information, Clearly Explained!!!16 мин
- The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9 мин
- Linear Regression, Clearly Explained!!!27 мин
Уроки 11–2010 уроков · 2 ч 12 мин
- Multiple Regression, Clearly Explained!!!5 мин
- Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12 мин
- Design Matrices For Linear Models, Clearly Explained!!!15 мин
- Odds and Log(Odds), Clearly Explained!!!12 мин
- Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16 мин
- StatQuest: Logistic Regression9 мин
- Logistic Regression Details Pt1: Coefficients19 мин
- Logistic Regression Details Pt 2: Maximum Likelihood10 мин
- Logistic Regression Details Pt 3: R-squared and p-value15 мин
- Saturated Models and Deviance19 мин
Уроки 21–3010 уроков · 2 ч 18 мин
- Logistic Regression in R, Clearly Explained!!!!17 мин
- Deviance Residuals6 мин
- ROC and AUC, Clearly Explained!16 мин
- ROC and AUC in R15 мин
- Regularization Part 1: Ridge (L2) Regression20 мин
- Regularization Part 2: Lasso (L1) Regression8 мин
- Ridge vs Lasso Regression, Visualized!!!9 мин
- Regularization Part 3: Elastic Net Regression5 мин
- Ridge, Lasso and Elastic-Net Regression in R18 мин
- StatQuest: Principal Component Analysis (PCA), Step-by-Step22 мин
Уроки 31–4010 уроков · 1 ч 32 мин
- StatQuest: PCA main ideas in only 5 minutes!!!6 мин
- StatQuest: PCA - Practical Tips8 мин
- StatQuest: PCA in R9 мин
- StatQuest: PCA in Python12 мин
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15 мин
- Bam!!! Clearly Explained!!!3 мин
- StatQuest: MDS and PCoA8 мин
- StatQuest: MDS and PCoA in R8 мин
- StatQuest: t-SNE, Clearly Explained12 мин
- StatQuest: Hierarchical Clustering11 мин
Уроки 41–5010 уроков · 2 ч 6 мин
- StatQuest: K-means clustering9 мин
- Clustering with DBSCAN, Clearly Explained!!!10 мин
- StatQuest: K-nearest neighbors, Clearly Explained6 мин
- Naive Bayes, Clearly Explained!!!15 мин
- Gaussian Naive Bayes, Clearly Explained!!!9 мин
- Decision and Classification Trees, Clearly Explained!!!18 мин
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5 мин
- Regression Trees, Clearly Explained!!!23 мин
- How to Prune Regression Trees, Clearly Explained!!!16 мин
- One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15 мин
Уроки 51–6010 уроков · 3 ч 39 мин
- Classification Trees in Python from Start to Finish1 ч 6 мин
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating10 мин
- StatQuest: Random Forests Part 2: Missing data and clustering11 мин
- StatQuest: Random Forests in R15 мин
- The Chain Rule, Clearly Explained!!!18 мин
- Gradient Descent, Step-by-Step24 мин
- Stochastic Gradient Descent, Clearly Explained!!!11 мин
- AdaBoost, Clearly Explained21 мин
- Gradient Boost Part 1 (of 4): Regression Main Ideas16 мин
- Gradient Boost Part 2 (of 4): Regression Details27 мин
Уроки 61–7010 уроков · 4 ч 4 мин
- Gradient Boost Part 3 (of 4): Classification17 мин
- Gradient Boost Part 4 (of 4): Classification Details37 мин
- Troll 2, Clearly Explained!!!5 мин
- XGBoost Part 1 (of 4): Regression26 мин
- XGBoost Part 2 (of 4): Classification25 мин
- XGBoost Part 3 (of 4): Mathematical Details27 мин
- XGBoost Part 4 (of 4): Crazy Cool Optimizations24 мин
- XGBoost in Python from Start to Finish57 мин
- CatBoost Part 1: Ordered Target Encoding9 мин
- CatBoost Part 2: Building and Using Trees16 мин
Уроки 71–8010 уроков · 2 ч 56 мин
- Cosine Similarity, Clearly Explained!!!10 мин
- Support Vector Machines Part 1 (of 3): Main Ideas!!!21 мин
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7 мин
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16 мин
- Support Vector Machines in Python from Start to Finish.45 мин
- The Essential Main Ideas of Neural Networks19 мин
- Neural Networks Pt. 2: Backpropagation Main Ideas18 мин
- Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.19 мин
- Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13 мин
- Neural Networks Pt. 3: ReLU In Action!!!9 мин
Уроки 81–9010 уроков · 2 ч 33 мин
- Neural Networks Pt. 4: Multiple Inputs and Outputs14 мин
- Neural Networks Part 5: ArgMax and SoftMax14 мин
- The SoftMax Derivative, Step-by-Step!!!7 мин
- Neural Networks Part 6: Cross Entropy10 мин
- Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22 мин
- Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15 мин
- Recurrent Neural Networks (RNNs), Clearly Explained!!!17 мин
- Long Short-Term Memory (LSTM), Clearly Explained21 мин
- Word Embedding and Word2Vec, Clearly Explained!!!16 мин
- Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!17 мин
Уроки 91–10010 уроков · 4 ч 9 мин
- Attention for Neural Networks, Clearly Explained!!!16 мин
- Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36 мин
- Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!37 мин
- Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!19 мин
- Tensors for Neural Networks, Clearly Explained!!!10 мин
- Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30 мин
- The matrix math behind transformer neural networks, one step at a time!!!24 мин
- The StatQuest Introduction to PyTorch23 мин
- Introduction to Coding Neural Networks with PyTorch and Lightning21 мин
- Long Short-Term Memory with PyTorch + Lightning33 мин
Уроки 101–1066 уроков · 2 ч 28 мин
- Word Embedding in PyTorch + Lightning32 мин
- Coding a ChatGPT Like Transformer From Scratch in PyTorch31 мин
- Reinforcement Learning: Essential Concepts18 мин
- Reinforcement Learning with Neural Networks: Essential Concepts24 мин
- Reinforcement Learning with Neural Networks: Mathematical Details25 мин
- Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18 мин
Что нужно
- Нужен опыт: курс продвинутого уровня.
Описание
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.
Кому подойдёт курс
- Тем, кто уже работает с Machine learning.
Эти два пункта — наш вывод из уровня курса. Описание принадлежит автору.