Quick and easy videos to learn machine learning and data science! Please consider joining the Dr. Data Science team!

Topics that we cover include classification, regression, clustering, exploratory data analysis, statistics, neural networks/deep learning, computer vision, Scikit-learn, PyTorch, and many more!

Introduction to Generative Models (Definition, Variational Autoencoders, and Diffusion Models )
16:23
Introduction to Generative Models (Definition, Variational Autoencoders, and Diffusion Models )
Random Forest vs. Gradient Boosting (RandomForestRegressor vs. GradientBoostingRegressor)
24:04
Random Forest vs. Gradient Boosting (RandomForestRegressor vs. GradientBoostingRegressor)
Decision Trees and Random Forests
37:21
Decision Trees and Random Forests
Underfitting vs. Overfitting: The “Decision Tree” Edition
11:16
Underfitting vs. Overfitting: The “Decision Tree” Edition
GPU Accelerated Scikit-learn for Machine Learning and Data Science with NVIDIA's cuML
12:30
GPU Accelerated Scikit-learn for Machine Learning and Data Science with NVIDIA's cuML
Support Vector Classifiers and Regressors & Margin (Theory and Implementation) (sklearn.svm.SVC/SVR)
52:17
Support Vector Classifiers and Regressors & Margin (Theory and Implementation) (sklearn.svm.SVC/SVR)
Clustering Methods: DBSCAN, Gaussian Mixtures, and Normalized Mutual Information
31:08
Clustering Methods: DBSCAN, Gaussian Mixtures, and Normalized Mutual Information
Explainable AI (XAI) and Shapley Additive Explanations (SHAP)
21:17
Explainable AI (XAI) and Shapley Additive Explanations (SHAP)
K-Means clustering: loss function, sklearn.cluster.KMeans, number of clusters
41:33
K-Means clustering: loss function, sklearn.cluster.KMeans, number of clusters
Multinomial logistic regression and micro vs macro average in scikit-learn
22:04
Multinomial logistic regression and micro vs macro average in scikit-learn
Evaluating Classifiers: Confusion Matrix, Precision, and Recall
33:16
Evaluating Classifiers: Confusion Matrix, Precision, and Recall
Python Package Managers: pip, conda, and uv | Data Science Digest
4:13
Python Package Managers: pip, conda, and uv | Data Science Digest
Logistic Regression, Sigmoid Function, Decision Boundary, Cross-Entropy Loss Function
41:59
Logistic Regression, Sigmoid Function, Decision Boundary, Cross-Entropy Loss Function
Bias-Variance Tradeoff in Machine Learning and Regularization (L1 vs L2)
29:51
Bias-Variance Tradeoff in Machine Learning and Regularization (L1 vs L2)
(Stochastic) Gradient Descent, learning rate, epochs, and SGDRegressor
38:29
(Stochastic) Gradient Descent, learning rate, epochs, and SGDRegressor
Linear regression implementation in scikit-learn & evaluation metrics: R2 score, explained variance
27:23
Linear regression implementation in scikit-learn & evaluation metrics: R2 score, explained variance
Linear regression fundamentals: loss function, gradient, and optimization with Python example
37:56
Linear regression fundamentals: loss function, gradient, and optimization with Python example
Creating Machine Learning Workflows Using Pipeline in Scikit-Learn
8:26
Creating Machine Learning Workflows Using Pipeline in Scikit-Learn
Train-Test Split and Cross-Validation, sklearn.model_selection, cross_val_score, and cross_validate
32:18
Train-Test Split and Cross-Validation, sklearn.model_selection, cross_val_score, and cross_validate
Feature Engineering Using The sklearn.preprocessing Package
14:54
Feature Engineering Using The sklearn.preprocessing Package
Data Loading, Understanding, and Filtering for Machine Learning Using Pandas
32:36
Data Loading, Understanding, and Filtering for Machine Learning Using Pandas
Basics of Python and Object-Oriented Programming With Simple Linear Regression
46:03
Basics of Python and Object-Oriented Programming With Simple Linear Regression
Empirical Risk Minimization and General Machine Learning Workflow
25:27
Empirical Risk Minimization and General Machine Learning Workflow
Components of Machine Learning: Data, Models, Training, and Evaluation
14:30
Components of Machine Learning: Data, Models, Training, and Evaluation
Definition of Machine Learning (Experience, Task, and Performance)
5:41
Definition of Machine Learning (Experience, Task, and Performance)
Statistical Machine Learning,Week 14: Recurrent Neural Network (LSTM & Backpropagation through time)
33:34
Statistical Machine Learning,Week 14: Recurrent Neural Network (LSTM & Backpropagation through time)
Generative AI and LLMs for Absolute Beginners (tokenization simplified!!!)
4:57
Generative AI and LLMs for Absolute Beginners (tokenization simplified!!!)
Apple M2 Max vs NVIDIA T4 GPU: Which is Best for Deep Learning?
2:19
Apple M2 Max vs NVIDIA T4 GPU: Which is Best for Deep Learning?
Statistical Machine Learning, Week 12: Hyperparameter Optimization, KerasTuner, Hyperband Example
33:23
Statistical Machine Learning, Week 12: Hyperparameter Optimization, KerasTuner, Hyperband Example
Statistical Machine Learning, Week 11: Implementing Convolutional Neural Networks (CNNs) using Keras
36:46
Statistical Machine Learning, Week 11: Implementing Convolutional Neural Networks (CNNs) using Keras