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Il corso include
- 29 h 51 min di video
- 106 lezioni
- Livello: Avanzato
- Lingua: English
- Ricordiamo dove eri arrivato
- Del tutto gratis, senza carta
Lezioni
11 sezioni · 106 lezioni · 29 h 51 min durata totale
Lezioni 1–1010 lezioni · 1 h 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
Lezioni 11–2010 lezioni · 2 h 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
Lezioni 21–3010 lezioni · 2 h 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
Lezioni 31–4010 lezioni · 1 h 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
Lezioni 41–5010 lezioni · 2 h 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
Lezioni 51–6010 lezioni · 3 h 39 min
- Classification Trees in Python from Start to Finish1 h 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
Lezioni 61–7010 lezioni · 4 h 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
Lezioni 71–8010 lezioni · 2 h 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
Lezioni 81–9010 lezioni · 2 h 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
Lezioni 91–10010 lezioni · 4 h 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
Lezioni 101–1066 lezioni · 2 h 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
Cosa serve
- Serve esperienza: il corso è di livello avanzato.
Descrizione
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.
A chi è rivolto
- Chi lavora già con Machine learning.
Questi due punti sono una nostra lettura del livello. La descrizione è dell'autore.