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This course includes
- 29h 51m of video
- 106 lessons
- Level: Advanced
- Spoken in English
- Your place is remembered
- Free in full, no card
Lessons
11 sections · 106 lessons · 29h 51m total length
Lessons 1 to 1010 lessons · 1h 55m
- A Gentle Introduction to Machine Learning13m
- Machine Learning Fundamentals: Cross Validation6m
- Machine Learning Fundamentals: The Confusion Matrix7m
- Machine Learning Fundamentals: Sensitivity and Specificity12m
- The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1m
- Machine Learning Fundamentals: Bias and Variance7m
- Entropy (for data science) Clearly Explained!!!17m
- Mutual Information, Clearly Explained!!!16m
- The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9m
- Linear Regression, Clearly Explained!!!27m
Lessons 11 to 2010 lessons · 2h 12m
- Multiple Regression, Clearly Explained!!!5m
- Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12m
- Design Matrices For Linear Models, Clearly Explained!!!15m
- Odds and Log(Odds), Clearly Explained!!!12m
- Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16m
- StatQuest: Logistic Regression9m
- Logistic Regression Details Pt1: Coefficients19m
- Logistic Regression Details Pt 2: Maximum Likelihood10m
- Logistic Regression Details Pt 3: R-squared and p-value15m
- Saturated Models and Deviance19m
Lessons 21 to 3010 lessons · 2h 18m
- Logistic Regression in R, Clearly Explained!!!!17m
- Deviance Residuals6m
- ROC and AUC, Clearly Explained!16m
- ROC and AUC in R15m
- Regularization Part 1: Ridge (L2) Regression20m
- Regularization Part 2: Lasso (L1) Regression8m
- Ridge vs Lasso Regression, Visualized!!!9m
- Regularization Part 3: Elastic Net Regression5m
- Ridge, Lasso and Elastic-Net Regression in R18m
- StatQuest: Principal Component Analysis (PCA), Step-by-Step22m
Lessons 31 to 4010 lessons · 1h 32m
- StatQuest: PCA main ideas in only 5 minutes!!!6m
- StatQuest: PCA - Practical Tips8m
- StatQuest: PCA in R9m
- StatQuest: PCA in Python12m
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15m
- Bam!!! Clearly Explained!!!3m
- StatQuest: MDS and PCoA8m
- StatQuest: MDS and PCoA in R8m
- StatQuest: t-SNE, Clearly Explained12m
- StatQuest: Hierarchical Clustering11m
Lessons 41 to 5010 lessons · 2h 6m
- StatQuest: K-means clustering9m
- Clustering with DBSCAN, Clearly Explained!!!10m
- StatQuest: K-nearest neighbors, Clearly Explained6m
- Naive Bayes, Clearly Explained!!!15m
- Gaussian Naive Bayes, Clearly Explained!!!9m
- Decision and Classification Trees, Clearly Explained!!!18m
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5m
- Regression Trees, Clearly Explained!!!23m
- How to Prune Regression Trees, Clearly Explained!!!16m
- One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15m
Lessons 51 to 6010 lessons · 3h 39m
- Classification Trees in Python from Start to Finish1h 6m
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating10m
- StatQuest: Random Forests Part 2: Missing data and clustering11m
- StatQuest: Random Forests in R15m
- The Chain Rule, Clearly Explained!!!18m
- Gradient Descent, Step-by-Step24m
- Stochastic Gradient Descent, Clearly Explained!!!11m
- AdaBoost, Clearly Explained21m
- Gradient Boost Part 1 (of 4): Regression Main Ideas16m
- Gradient Boost Part 2 (of 4): Regression Details27m
Lessons 61 to 7010 lessons · 4h 4m
- Gradient Boost Part 3 (of 4): Classification17m
- Gradient Boost Part 4 (of 4): Classification Details37m
- Troll 2, Clearly Explained!!!5m
- XGBoost Part 1 (of 4): Regression26m
- XGBoost Part 2 (of 4): Classification25m
- XGBoost Part 3 (of 4): Mathematical Details27m
- XGBoost Part 4 (of 4): Crazy Cool Optimizations24m
- XGBoost in Python from Start to Finish57m
- CatBoost Part 1: Ordered Target Encoding9m
- CatBoost Part 2: Building and Using Trees16m
Lessons 71 to 8010 lessons · 2h 56m
- Cosine Similarity, Clearly Explained!!!10m
- Support Vector Machines Part 1 (of 3): Main Ideas!!!21m
- Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7m
- Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16m
- Support Vector Machines in Python from Start to Finish.45m
- The Essential Main Ideas of Neural Networks19m
- Neural Networks Pt. 2: Backpropagation Main Ideas18m
- Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.19m
- Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13m
- Neural Networks Pt. 3: ReLU In Action!!!9m
Lessons 81 to 9010 lessons · 2h 33m
- Neural Networks Pt. 4: Multiple Inputs and Outputs14m
- Neural Networks Part 5: ArgMax and SoftMax14m
- The SoftMax Derivative, Step-by-Step!!!7m
- Neural Networks Part 6: Cross Entropy10m
- Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22m
- Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15m
- Recurrent Neural Networks (RNNs), Clearly Explained!!!17m
- Long Short-Term Memory (LSTM), Clearly Explained21m
- Word Embedding and Word2Vec, Clearly Explained!!!16m
- Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!17m
Lessons 91 to 10010 lessons · 4h 9m
- Attention for Neural Networks, Clearly Explained!!!16m
- Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36m
- Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!37m
- Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!19m
- Tensors for Neural Networks, Clearly Explained!!!10m
- Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30m
- The matrix math behind transformer neural networks, one step at a time!!!24m
- The StatQuest Introduction to PyTorch23m
- Introduction to Coding Neural Networks with PyTorch and Lightning21m
- Long Short-Term Memory with PyTorch + Lightning33m
Lessons 101 to 1066 lessons · 2h 28m
- Word Embedding in PyTorch + Lightning32m
- Coding a ChatGPT Like Transformer From Scratch in PyTorch31m
- Reinforcement Learning: Essential Concepts18m
- Reinforcement Learning with Neural Networks: Essential Concepts24m
- Reinforcement Learning with Neural Networks: Mathematical Details25m
- Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18m
What you need
- Experience expected: this course is pitched at an advanced level.
Description
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.
Who this course is for
- People already working with Machine learning.
Those two are our reading of the course's level. The description is the author's own.