This course on Machine Learning aims to provide a comprehensive understanding of various machine learning concepts and techniques. By the end of the course, learners will be able to:
- Understand the fundamentals of machine learning, including cross-validation, confusion matrix, sensitivity, and specificity.
- Apply linear regression, multiple regression, logistic regression, and regularization techniques.
- Implement dimensionality reduction methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
- Explore clustering algorithms like K-means, hierarchical clustering, and support vector machines.
- Gain knowledge of ensemble methods like Random Forests, AdaBoost, and XGBoost.
- Learn about neural networks, backpropagation, convolutional neural networks, and image classification.
The course utilizes a tutorial-based teaching method with step-by-step explanations and practical tips. It is designed for individuals interested in delving into the field of machine learning, data science, or artificial intelligence.
Overview
Syllabus
A Gentle Introduction to Machine Learning.
Machine Learning Fundamentals: Cross Validation.
Machine Learning Fundamentals: The Confusion Matrix.
Machine Learning Fundamentals: Sensitivity and Specificity.
Machine Learning Fundamentals: Bias and Variance.
Entropy (for data science) Clearly Explained!!!.
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.).
Linear Regression, Clearly Explained!!!.
Multiple Regression, Clearly Explained!!!.
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!.
Design Matrices For Linear Models, Clearly Explained!!!.
ROC and AUC, Clearly Explained!.
ROC and AUC in R.
Odds and Log(Odds), Clearly Explained!!!.
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!.
StatQuest: Logistic Regression.
Logistic Regression Details Pt1: Coefficients.
Logistic Regression Details Pt 2: Maximum Likelihood.
Logistic Regression Details Pt 3: R-squared and p-value.
Saturated Models and Deviance.
Logistic Regression in R, Clearly Explained!!!!.
Deviance Residuals.
Regularization Part 1: Ridge (L2) Regression.
Regularization Part 2: Lasso (L1) Regression.
Ridge vs Lasso Regression, Visualized!!!.
Regularization Part 3: Elastic Net Regression.
Ridge, Lasso and Elastic-Net Regression in R.
StatQuest: Principal Component Analysis (PCA), Step-by-Step.
StatQuest: PCA main ideas in only 5 minutes!!!.
StatQuest: PCA - Practical Tips.
StatQuest: PCA in R.
StatQuest: PCA in Python.
StatQuest: Linear Discriminant Analysis (LDA) clearly explained..
Bam!!! Clearly Explained!!!.
StatQuest: MDS and PCoA.
StatQuest: MDS and PCoA in R.
StatQuest: t-SNE, Clearly Explained.
StatQuest: Hierarchical Clustering.
StatQuest: K-means clustering.
StatQuest: K-nearest neighbors, Clearly Explained.
Naive Bayes, Clearly Explained!!!.
Gaussian Naive Bayes, Clearly Explained!!!.
Decision and Classification Trees, Clearly Explained!!!.
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data.
Regression Trees, Clearly Explained!!!.
How to Prune Regression Trees, Clearly Explained!!!.
Classification Trees in Python from Start to Finish.
StatQuest: Random Forests Part 1 - Building, Using and Evaluating.
StatQuest: Random Forests Part 2: Missing data and clustering.
StatQuest: Random Forests in R.
The Chain Rule.
Gradient Descent, Step-by-Step.
Stochastic Gradient Descent, Clearly Explained!!!.
AdaBoost, Clearly Explained.
Gradient Boost Part 1 (of 4): Regression Main Ideas.
Gradient Boost Part 2 (of 4): Regression Details.
Gradient Boost Part 3 (of 4): Classification.
Gradient Boost Part 4 (of 4): Classification Details.
Troll 2, Clearly Explained!!!.
Support Vector Machines Part 1 (of 3): Main Ideas!!!.
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3).
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3).
Support Vector Machines in Python from Start to Finish..
XGBoost Part 1 (of 4): Regression.
XGBoost Part 2 (of 4): Classification.
XGBoost Part 3 (of 4): Mathematical Details.
XGBoost Part 4 (of 4): Crazy Cool Optimizations.
XGBoost in Python from Start to Finish.
Neural Networks Pt. 1: Inside the Black Box.
Neural Networks Pt. 2: Backpropagation Main Ideas.
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously..
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule.
Neural Networks Pt. 3: ReLU In Action!!!.
Neural Networks Pt. 4: Multiple Inputs and Outputs.
Neural Networks Part 5: ArgMax and SoftMax.
The SoftMax Derivative, Step-by-Step!!!.
Neural Networks Part 6: Cross Entropy.
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation.
Neural Networks Part 8: Image Classification with Convolutional Neural Networks.
Tensors for Neural Networks, Clearly Explained!!!.
Lowess and Loess, Clearly Explained!!!.
Population and Estimated Parameters, Clearly Explained!!!.
Clustering with DBSCAN, Clearly Explained!!!.
Taught by
StatQuest with Josh Starmer
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Reviews
4.0 rating, based on 1 Class Central review
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Overall I really like this class because all lectures, assignments, and tests are straight forward. A couple things I dislike about the class are that there should be more opportunities for extra credit and it would be awesome if the final was an objective essay about what we have learned in this class or what we like about the class. I believe that I have more knowledge about ocean, weather, and marine lives and hopefully, I can use them in real life.
