Data & AI

Machine Learning (StatQuest with Josh Starmer)

Sikhami InstituteEnglish

Free

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  • 106 video lectures (29h 50m)
  • Certificate of completion
  • Ask the teacher your doubts
  • Watch on the web and in the Android app

About this course

Machine learning from the ground up, clearly explained. Josh Starmer of StatQuest breaks every idea into small, simple steps with pictures, from fitting a line to data all the way to neural networks and the transformers behind ChatGPT. Who it's for: students and working professionals starting out in data science or AI. School maths is enough to follow along; a few videos code in R or Python. These are the free, official videos of the "Machine Learning" playlist from the StatQuest with Josh Starmer YouTube channel (youtube.com/@statquest). All videos belong to StatQuest; Sikhami is not affiliated with StatQuest and simply arranges the public playlist into a course, so you can learn one lesson at a time and keep track of your progress.

What you will learn

• ML fundamentals: cross validation, the confusion matrix, sensitivity and specificity, bias and variance, ROC and AUC • Linear and logistic regression, and regularisation (ridge, lasso, elastic net) • PCA, t-SNE, clustering (k-means, hierarchical, DBSCAN) and k-nearest neighbours • Naive Bayes, decision trees, random forests and support vector machines • Gradient descent, AdaBoost, gradient boosting, XGBoost and CatBoost • Neural networks and backpropagation, CNNs, RNNs and LSTMs, word embeddings, attention and transformers • Coding neural networks with PyTorch, and reinforcement learning (including RLHF)

Course content

1 section · 106 lectures · 29h 50m

  1. 1. Machine Learning

    • A Gentle Introduction to Machine Learning12m
    • Machine Learning Fundamentals: Cross Validation6m
    • Machine Learning Fundamentals: The Confusion Matrix7m
    • Machine Learning Fundamentals: Sensitivity and Specificity11m
    • The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!0m
    • Machine Learning Fundamentals: Bias and Variance6m
    • Entropy (for data science) Clearly Explained!!!16m
    • 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
    • Multiple Regression, Clearly Explained!!!5m
    • Using Linear Models for t-tests and ANOVA, Clearly Explained!!!11m
    • Design Matrices For Linear Models, Clearly Explained!!!14m
    • Odds and Log(Odds), Clearly Explained!!!11m
    • Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16m
    • StatQuest: Logistic Regression8m
    • 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 Deviance18m
    • 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 R17m
    • StatQuest: Principal Component Analysis (PCA), Step-by-Step21m
    • StatQuest: PCA main ideas in only 5 minutes!!!6m
    • StatQuest: PCA - Practical Tips8m
    • StatQuest: PCA in R8m
    • StatQuest: PCA in Python11m
    • StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15m
    • Bam!!! Clearly Explained!!!2m
    • StatQuest: MDS and PCoA8m
    • StatQuest: MDS and PCoA in R7m
    • StatQuest: t-SNE, Clearly Explained11m
    • StatQuest: Hierarchical Clustering11m
    • StatQuest: K-means clustering8m
    • Clustering with DBSCAN, Clearly Explained!!!9m
    • StatQuest: K-nearest neighbors, Clearly Explained5m
    • 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!!!22m
    • How to Prune Regression Trees, Clearly Explained!!!16m
    • One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15m
    • Classification Trees in Python from Start to Finish1h 6m
    • StatQuest: Random Forests Part 1 - Building, Using and Evaluating9m
    • StatQuest: Random Forests Part 2: Missing data and clustering10m
    • StatQuest: Random Forests in R15m
    • The Chain Rule, Clearly Explained!!!18m
    • Gradient Descent, Step-by-Step23m
    • Stochastic Gradient Descent, Clearly Explained!!!10m
    • AdaBoost, Clearly Explained20m
    • Gradient Boost Part 1 (of 4): Regression Main Ideas15m
    • Gradient Boost Part 2 (of 4): Regression Details26m
    • 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): Regression25m
    • 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 Finish56m
    • CatBoost Part 1: Ordered Target Encoding8m
    • CatBoost Part 2: Building and Using Trees16m
    • Cosine Similarity, Clearly Explained!!!10m
    • Support Vector Machines Part 1 (of 3): Main Ideas!!!20m
    • 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)15m
    • Support Vector Machines in Python from Start to Finish.44m
    • The Essential Main Ideas of Neural Networks18m
    • Neural Networks Pt. 2: Backpropagation Main Ideas17m
    • Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.18m
    • Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13m
    • Neural Networks Pt. 3: ReLU In Action!!!8m
    • Neural Networks Pt. 4: Multiple Inputs and Outputs13m
    • Neural Networks Part 5: ArgMax and SoftMax14m
    • The SoftMax Derivative, Step-by-Step!!!7m
    • Neural Networks Part 6: Cross Entropy9m
    • 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!!!16m
    • Long Short-Term Memory (LSTM), Clearly Explained20m
    • Word Embedding and Word2Vec, Clearly Explained!!!16m
    • Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!16m
    • Attention for Neural Networks, Clearly Explained!!!15m
    • Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36m
    • Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!36m
    • Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!18m
    • Tensors for Neural Networks, Clearly Explained!!!9m
    • Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30m
    • The matrix math behind transformer neural networks, one step at a time!!!23m
    • The StatQuest Introduction to PyTorch23m
    • Introduction to Coding Neural Networks with PyTorch and Lightning20m
    • Long Short-Term Memory with PyTorch + Lightning33m
    • 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