Trung cấpMachine LearningTiếng Anh🤖 Phản hồi cá nhân hoá

Fundamental of Machine Learning

Giảng dạy bởi Nguyễn Hùng Việt

Module

Bài học

Về khóa học

3 module · 54 bài

Nội dung khóa học

3 module · 54 bài

Module 1: Data Preparation and Model Foundations
21 bài
1. 1
  Course Syllabus
  Course SyllabusCourse Goals, Content, and Assessment
2. 2
  Course Overview▶ Video · 1 phút
  Overview of Machine Learning and Data Science
3. 3
  Lecture Slides
  Lecture Slides
4. 4
  1.1 How Machines Learn▶ Video · 6 phút
  1.1 How Machines LearnHow Machines Learn
5. 5
  1.2 Data Types▶ Video · 6 phút
  1.2 Data TypesTypes of Data
6. 6
  1.3 Labeled Data and the Three Groups of Algorithms▶ Video · 5 phút
  1.3 Labeled Data and the Three Groups of AlgorithmsLabeled Data and the Three Types of Learning
7. 7
  1.4 Regression, classification, clustering, association▶ Video · 3 phút
  1.4 Regression, classification, clustering, associationRegression, Classification, Clustering, Association
8. 8
  1.5 Feature Types▶ Video · 5 phút
  1.5 Feature TypesTypes of Features
9. 9
  1.6 Data Table Terminology and Data Splitting▶ Video · 6 phút
  1.6 Data Table Terminology and Data SplittingDataset Terminology and Splitting
10. 10
  1.7 Data Quality▶ Video · 6 phút
  1.7 Data QualityData Quality
11. 11
  1.8 Normalizing Numerical Features▶ Video · 4 phút
  Scaling Numerical Features1.8 Normalizing Numerical Features
12. 12
  1.9 Encoding Categorical Features▶ Video · 4 phút
  1.9 Encoding Categorical FeaturesEncoding Categorical Features
13. 13
  1.10 Machine Learning Pipeline▶ Video · 3 phút
  1.10 Machine Learning PipelineThe 9-Step Machine Learning Pipeline
14. 14
  1.11 Lab: Handling Missing Values and Pipelines
  1.11 Lab: Handling Missing Values and PipelinesImputing Missing Values and Pipelines
15. 15
  1.12 Lab: Bias-Variance Through Experiments
  1.12 Lab: Bias-Variance Through ExperimentsBias-Variance in Practice
16. 16
  Capstone Practice: Planning Preprocessing for a Real Dataset
  Capstone Practice: Planning Preprocessing for a Real DatasetMachine Learning Foundations: The Training Loop and Loss Functions
17. 17
  Handling Missing Values
  Handling Missing Values
18. 18
  Feature Scaling & Encoding
  Feature Scaling and Categorical Encoding
19. 19
  Data Partitioning
  Data Splitting
20. 20
  Supervised Learning Foundations and the Bias-Variance Tradeoff
  Supervised Learning FoundationsBias-Variance Tradeoff
21. 21
  Machine Learning Pipelines in Scikit-learn
  Machine Learning Pipelines in Scikit-learn
Module 2: Core Machine Learning Algorithms
13 bài
1. 1
  2.1 Linear Regression▶ Video · 4 phút
  Linear Regression2.1 Linear Regression
2. 2
  2.2 Logistic Regression▶ Video · 3 phút
  2.2 Logistic RegressionLogistic Regression and the Sigmoid Function
3. 3
  2.3 Decision Trees and the Loan Approval Problem▶ Video · 7 phút
  2.3 Decision Trees and the Loan Approval ProblemDecision Tree Structure
4. 4
  2.4 Gini impurity▶ Video · 4 phút
  2.4 Gini impurityGini Impurity and Choosing a Split Feature
5. 5
  2.5 Bootstrapping and Random Forest▶ Video · 6 phút
  Bootstrapping and Random Forest2.5 Bootstrapping and Random Forest
6. 6
  2.6 SVM: Which Separating Line to Choose▶ Video · 3 phút
  SVM: Choosing a Decision Boundary2.6 SVM: Which Separating Line to Choose
7. 7
  2.7 SVM: Maximum Margin and Support Vectors▶ Video · 5 phút
  2.7 SVM: Maximum Margin and Support VectorsSVM: Maximum Margin and Support Vectors
8. 8
  2.8 SVM: The Kernel Trick▶ Video · 4 phút
  SVM: The Kernel Trick2.8 SVM: The Kernel Trick
9. 9
  Capstone Practice: Choosing an Algorithm for Your Problem
  Capstone Practice: Choosing an Algorithm for Your ProblemCore Machine Learning Algorithms
10. 10
  Linear Regression
  Linear Regression
11. 11
  Decision Trees
  Decision Trees
12. 12
  Support Vector Machines
  Support Vector Machines (SVM)
13. 13
  Choosing an Algorithm: Assumptions and Use Cases
  Choosing the Right Algorithm
Module 3: Model Evaluation and Optimization
20 bài
1. 1
  3.1 Accuracy and Imbalanced Data▶ Video · 4 phút
  3.1 Accuracy and Imbalanced DataAccuracy and Imbalanced Data
2. 2
  3.2 Confusion Matrix▶ Video · 5 phút
  Confusion Matrix3.2 Confusion Matrix
3. 3
  3.3 Precision, Recall, and F1 Score▶ Video · 5 phút
  3.3 Precision, Recall, and F1 ScorePrecision, Recall, and F1 Score
4. 4
  3.4 Mean Absolute Error (MAE)▶ Video · 5 phút
  3.4 Mean Absolute Error (MAE)Mean Absolute Error (MAE)
5. 5
  3.5 Coefficient of Determination R²▶ Video · 3 phút
  3.5 Coefficient of Determination R²Coefficient of Determination (R²)
6. 6
  3.6 MSE and RMSE
  3.6 MSE and RMSEMSE and RMSE
7. 7
  3.7 Lab: Cross-Validation and GridSearchCV
  3.7 Lab: Cross-Validation and GridSearchCVCross-Validation and GridSearchCV
8. 8
  Capstone Practice: Choosing Evaluation Metrics for Three Scenarios
  Capstone Practice: Choosing Evaluation Metrics for Three ScenariosModel Evaluation
9. 9
  Evaluation Metrics
  Model Evaluation Metrics
10. 10
  Cross-Validation
  Cross-Validation
11. 11
  Hyperparameter Tuning
  Hyperparameter Tuning
12. 12
  Applying the Complete Machine Learning Workflow
  The Complete Machine Learning Workflow
13. 13
  3.P1 Loading Data and Splitting x, y▶ Video · 6 phút
  Loading CSV with pandas and Separating x and y3.P1 Loading Data and Splitting x, y
14. 14
  3.P2 Splitting Train/Test with train_test_split▶ Video · 4 phút
  Splitting Train/Test with train_test_split3.P2 Splitting Train/Test with train_test_split
15. 15
  3.P3 random_state and Reproducibility▶ Video · 4 phút
  random_state and Reproducible Splits3.P3 random_state and Reproducibility
16. 16
  3.P4 Standardization with StandardScaler▶ Video · 5 phút
  3.P4 Standardization with StandardScalerScaling with StandardScaler
17. 17
  3.P5 Training a Random Forest and Predicting▶ Video · 3 phút
  3.P5 Training a Random Forest and PredictingTraining and Predicting with RandomForestClassifier
18. 18
  3.P6 Reading the Evaluation Results▶ Video · 5 phút
  3.P6 Reading the Evaluation ResultsReading classification_report and confusion_matrix
19. 19
  Capstone Practice: Re-run the Entire Workflow on Your Own Data
  Capstone Practice: Re-run the Entire Workflow on Your Own DataHands-On Machine Learning Workflow with Scikit-learn
20. 20
  End-of-Course Knowledge Check
  End-of-Course Knowledge CheckMachine Learning Fundamentals Review

← Danh mục khoá học

Trung cấpMachine LearningTiếng Anh🤖 Phản hồi cá nhân hoá

Fundamental of Machine Learning

Giảng dạy bởi Nguyễn Hùng Việt

Module

Bài học

Về khóa học

Fundamental of Machine Learning

3 module · 54 bài

Nội dung khóa học

3 module · 54 bài

Module 1: Data Preparation and Model Foundations
21 bài
1. 1
  Course Syllabus
  Course SyllabusCourse Goals, Content, and Assessment
2. 2
  Course Overview▶ Video · 1 phút
  Overview of Machine Learning and Data Science
3. 3
  Lecture Slides
  Lecture Slides
4. 4
  1.1 How Machines Learn▶ Video · 6 phút
  1.1 How Machines LearnHow Machines Learn
5. 5
  1.2 Data Types▶ Video · 6 phút
  1.2 Data TypesTypes of Data
6. 6
  1.3 Labeled Data and the Three Groups of Algorithms▶ Video · 5 phút
  1.3 Labeled Data and the Three Groups of AlgorithmsLabeled Data and the Three Types of Learning
7. 7
  1.4 Regression, classification, clustering, association▶ Video · 3 phút
  1.4 Regression, classification, clustering, associationRegression, Classification, Clustering, Association
8. 8
  1.5 Feature Types▶ Video · 5 phút
  1.5 Feature TypesTypes of Features
9. 9
  1.6 Data Table Terminology and Data Splitting▶ Video · 6 phút
  1.6 Data Table Terminology and Data SplittingDataset Terminology and Splitting
10. 10
  1.7 Data Quality▶ Video · 6 phút
  1.7 Data QualityData Quality
11. 11
  1.8 Normalizing Numerical Features▶ Video · 4 phút
  Scaling Numerical Features1.8 Normalizing Numerical Features
12. 12
  1.9 Encoding Categorical Features▶ Video · 4 phút
  1.9 Encoding Categorical FeaturesEncoding Categorical Features
13. 13
  1.10 Machine Learning Pipeline▶ Video · 3 phút
  1.10 Machine Learning PipelineThe 9-Step Machine Learning Pipeline
14. 14
  1.11 Lab: Handling Missing Values and Pipelines
  1.11 Lab: Handling Missing Values and PipelinesImputing Missing Values and Pipelines
15. 15
  1.12 Lab: Bias-Variance Through Experiments
  1.12 Lab: Bias-Variance Through ExperimentsBias-Variance in Practice
16. 16
  Capstone Practice: Planning Preprocessing for a Real Dataset
  Capstone Practice: Planning Preprocessing for a Real DatasetMachine Learning Foundations: The Training Loop and Loss Functions
17. 17
  Handling Missing Values
  Handling Missing Values
18. 18
  Feature Scaling & Encoding
  Feature Scaling and Categorical Encoding
19. 19
  Data Partitioning
  Data Splitting
20. 20
  Supervised Learning Foundations and the Bias-Variance Tradeoff
  Supervised Learning FoundationsBias-Variance Tradeoff
21. 21
  Machine Learning Pipelines in Scikit-learn
  Machine Learning Pipelines in Scikit-learn
Module 2: Core Machine Learning Algorithms
13 bài
1. 1
  2.1 Linear Regression▶ Video · 4 phút
  Linear Regression2.1 Linear Regression
2. 2
  2.2 Logistic Regression▶ Video · 3 phút
  2.2 Logistic RegressionLogistic Regression and the Sigmoid Function
3. 3
  2.3 Decision Trees and the Loan Approval Problem▶ Video · 7 phút
  2.3 Decision Trees and the Loan Approval ProblemDecision Tree Structure
4. 4
  2.4 Gini impurity▶ Video · 4 phút
  2.4 Gini impurityGini Impurity and Choosing a Split Feature
5. 5
  2.5 Bootstrapping and Random Forest▶ Video · 6 phút
  Bootstrapping and Random Forest2.5 Bootstrapping and Random Forest
6. 6
  2.6 SVM: Which Separating Line to Choose▶ Video · 3 phút
  SVM: Choosing a Decision Boundary2.6 SVM: Which Separating Line to Choose
7. 7
  2.7 SVM: Maximum Margin and Support Vectors▶ Video · 5 phút
  2.7 SVM: Maximum Margin and Support VectorsSVM: Maximum Margin and Support Vectors
8. 8
  2.8 SVM: The Kernel Trick▶ Video · 4 phút
  SVM: The Kernel Trick2.8 SVM: The Kernel Trick
9. 9
  Capstone Practice: Choosing an Algorithm for Your Problem
  Capstone Practice: Choosing an Algorithm for Your ProblemCore Machine Learning Algorithms
10. 10
  Linear Regression
  Linear Regression
11. 11
  Decision Trees
  Decision Trees
12. 12
  Support Vector Machines
  Support Vector Machines (SVM)
13. 13
  Choosing an Algorithm: Assumptions and Use Cases
  Choosing the Right Algorithm
Module 3: Model Evaluation and Optimization
20 bài
1. 1
  3.1 Accuracy and Imbalanced Data▶ Video · 4 phút
  3.1 Accuracy and Imbalanced DataAccuracy and Imbalanced Data
2. 2
  3.2 Confusion Matrix▶ Video · 5 phút
  Confusion Matrix3.2 Confusion Matrix
3. 3
  3.3 Precision, Recall, and F1 Score▶ Video · 5 phút
  3.3 Precision, Recall, and F1 ScorePrecision, Recall, and F1 Score
4. 4
  3.4 Mean Absolute Error (MAE)▶ Video · 5 phút
  3.4 Mean Absolute Error (MAE)Mean Absolute Error (MAE)
5. 5
  3.5 Coefficient of Determination R²▶ Video · 3 phút
  3.5 Coefficient of Determination R²Coefficient of Determination (R²)
6. 6
  3.6 MSE and RMSE
  3.6 MSE and RMSEMSE and RMSE
7. 7
  3.7 Lab: Cross-Validation and GridSearchCV
  3.7 Lab: Cross-Validation and GridSearchCVCross-Validation and GridSearchCV
8. 8
  Capstone Practice: Choosing Evaluation Metrics for Three Scenarios
  Capstone Practice: Choosing Evaluation Metrics for Three ScenariosModel Evaluation
9. 9
  Evaluation Metrics
  Model Evaluation Metrics
10. 10
  Cross-Validation
  Cross-Validation
11. 11
  Hyperparameter Tuning
  Hyperparameter Tuning
12. 12
  Applying the Complete Machine Learning Workflow
  The Complete Machine Learning Workflow
13. 13
  3.P1 Loading Data and Splitting x, y▶ Video · 6 phút
  Loading CSV with pandas and Separating x and y3.P1 Loading Data and Splitting x, y
14. 14
  3.P2 Splitting Train/Test with train_test_split▶ Video · 4 phút
  Splitting Train/Test with train_test_split3.P2 Splitting Train/Test with train_test_split
15. 15
  3.P3 random_state and Reproducibility▶ Video · 4 phút
  random_state and Reproducible Splits3.P3 random_state and Reproducibility
16. 16
  3.P4 Standardization with StandardScaler▶ Video · 5 phút
  3.P4 Standardization with StandardScalerScaling with StandardScaler
17. 17
  3.P5 Training a Random Forest and Predicting▶ Video · 3 phút
  3.P5 Training a Random Forest and PredictingTraining and Predicting with RandomForestClassifier
18. 18
  3.P6 Reading the Evaluation Results▶ Video · 5 phút
  3.P6 Reading the Evaluation ResultsReading classification_report and confusion_matrix
19. 19
  Capstone Practice: Re-run the Entire Workflow on Your Own Data
  Capstone Practice: Re-run the Entire Workflow on Your Own DataHands-On Machine Learning Workflow with Scikit-learn
20. 20
  End-of-Course Knowledge Check
  End-of-Course Knowledge CheckMachine Learning Fundamentals Review