-
Day 1 19
-
IntroductionLecture1.1
-
AI Overview – History of AILecture1.2
-
AI Overview – Machine LearningLecture1.3
-
ตอบคำถามนักเรียน1Lecture1.4
-
AI Overview – Supervised LearningLecture1.5
-
Supervised Learning WorkflowLecture1.6
-
ตอบคำถามนักเรียน2Lecture1.7
-
Data Preparation – Data StatingLecture1.8
-
Data Preparation – Data RequirementLecture1.9
-
Data Preparation – Missing ValuesLecture1.10
-
Data Preparation – OutliersLecture1.11
-
Data Preparation – Type of Data & Ordinal EncodingLecture1.12
-
Data Preparation – One Hot EncodingLecture1.13
-
ตอบคำถามนักเรียน3Lecture1.14
-
Data Preparation – Feature ScalingLecture1.15
-
Data Preparation – ImageLecture1.16
-
Data Preparation – TextLecture1.17
-
ตอบคำถามนักเรียน4Lecture1.18
-
Data Preparation – SoundLecture1.19
-
-
Day 2 30
-
Agenda – Day2Lecture2.1
-
Linear Regression – What is LR?Lecture2.2
-
Linear Regression – Data for LR + Pros & ConsLecture2.3
-
Linear Regression – Real World ApplicationLecture2.4
-
Linear Regression – Assumption & Real Face of the ModelLecture2.5
-
Linear Regression – Cost Function & Cost LandscapeLecture2.6
-
Linear Regression – ที่มาของ Cost Landscape & การพิสูจน์ ConvexityLecture2.7
-
Linear Regression – How to Create Model (Math)Lecture2.8
-
Linear Regression – How to Create Model (Code) & Further ReadingLecture2.9
-
Linear Regression – PredictionLecture2.10
-
Model Evaluation – IntroductionLecture2.11
-
Model Evaluation – R-squaredLecture2.12
-
Model Evaluation – MSELecture2.13
-
Model Evaluation – MAELecture2.14
-
Model Evaluation – MAPELecture2.15
-
Model Evaluation – ConclusionLecture2.16
-
วิธีการเริ่มใช้งาน Jupyter NotebookLecture2.17
-
Code Pipeline – IntroductionLecture2.18
-
Code Pipeline – Import LibraryLecture2.19
-
Code Pipeline – Read DataLecture2.20
-
Code Pipeline – Clean DataLecture2.21
-
Code Pipeline – Split into Train & TestLecture2.22
-
Code Pipeline – Data PreparationLecture2.23
-
Code Pipeline – Create ModelLecture2.24
-
Code Pipeline – Prediction, Model EvaluationLecture2.25
-
ตอบคำถามนักเรียน1Lecture2.26
-
Linear Regression – AI in MarketingLecture2.27
-
Linear Regression – AI in InvestmentLecture2.28
-
ตอบคำถามนักเรียน2Lecture2.29
-
Linear Regression – AI in InsuranceLecture2.30
-
-
Day 3 31
-
Agenda – Day3Lecture3.1
-
Logistic Regression – OverviewLecture3.2
-
What is LoR (2-class)?Lecture3.3
-
Data for LoR (2-class) + Pros & ConsLecture3.4
-
Real World ApplicationLecture3.5
-
LoR (2-class) – AssumptionLecture3.6
-
LoR (2-class) – Real Face of the ModelLecture3.7
-
ตอบคำถามนักเรียน1Lecture3.8
-
LoR (2-class) – Cost Function & Cost LandscapeLecture3.9
-
LoR (2-class) – How to Create Model (Math) part1Lecture3.10
-
LoR (2-class) – How to Create Model (Math) part2Lecture3.11
-
LoR (2-class) – How to Create Model (Math) part3Lecture3.12
-
LoR (2-class) – How to Create Model (Code) + PredictionLecture3.13
-
ตอบคำถามนักเรียน2Lecture3.14
-
Logistic Regression with MulticlassLecture3.15
-
ขยายความ Logistic Regression with MulticlassLecture3.16
-
Why Softmax?Lecture3.17
-
สรุปให้ง่าย Logistic Regression with MulticlassLecture3.18
-
Data for LoR (multi-class) + Pros & Cons + RWALecture3.19
-
LoR (multi-class) – Assumption + Real Face of the ModelLecture3.20
-
LoR (multi-class) – Cost Function & Cost LandscapeLecture3.21
-
LoR (multi-class) – How to Create ModelLecture3.22
-
LoR (multi-class) – PredictionLecture3.23
-
สรุป Logistic RegressionLecture3.24
-
Model Evaluation – Confusion MatrixLecture3.25
-
Model Evaluation – AccuracyLecture3.26
-
Model Evaluation – Precision ScoreLecture3.27
-
Model Evaluation – Recall ScoreLecture3.28
-
Model Evaluation – Precision VS RecallLecture3.29
-
Model Evaluation – F1 ScoreLecture3.30
-
Model Evaluation – ConclusionLecture3.31
-
-
Day 4 16
-
LoR – Workshop OverviewLecture4.1
-
LoR – AI in HealthcareLecture4.2
-
LoR – Fake NewsLecture4.3
-
LoR – AI in Fruit IndustryLecture4.4
-
Introduction to Neural NetworkLecture4.5
-
Architecture of Neural NetworkLecture4.6
-
Component of Neural NetworkLecture4.7
-
How Neural Network WorkLecture4.8
-
Introduction to Deep LearningLecture4.9
-
Why we need Deep Learning?Lecture4.10
-
How Deep Learning WorkLecture4.11
-
Deep Learning for RegressionLecture4.12
-
Deep Learning for ClassificationLecture4.13
-
Deep Learning – Car PriceLecture4.14
-
Deep Learning – AI in Skin CancerLecture4.15
-
ตอบคำถามนักเรียนLecture4.16
-
-
Day 5 33
-
Agenda – Day5Lecture5.1
-
อธิบายภาพรวมเพิ่มเติมLecture5.2
-
What is Classification Tree?Lecture5.3
-
Data for Classification TreeLecture5.4
-
Pros & Cons + Real World ApplicationLecture5.5
-
How to Create Model (Math) part1Lecture5.6
-
How to Create Model (Math) part2Lecture5.7
-
ที่มาของ Gini ImpurityLecture5.8
-
How to Create Model (Math) part3Lecture5.9
-
How to Create Model (Math) part4Lecture5.10
-
ตอบคำถามนักเรียนLecture5.11
-
How to Create Model (Code)Lecture5.12
-
Further ReadingLecture5.13
-
PredictionLecture5.14
-
ตอบคำถามนักเรียน2Lecture5.15
-
Workshop – Hepatitis CLecture5.16
-
What is Regression Tree?Lecture5.17
-
Data for Regression TreeLecture5.18
-
Pros & Cons + Real World ApplicationLecture5.19
-
Assumption + Real Face of the ModelLecture5.20
-
How to Create Model (Math)Lecture5.21
-
How to Create Model (Code)Lecture5.22
-
PredictionLecture5.23
-
parameter vs hyperparameterLecture5.24
-
Workshop – AI in Civil EngineeringLecture5.25
-
What is KNN? + Data for KNNLecture5.26
-
Pros & Cons + Real World ApplicationLecture5.27
-
How to Create Model (Math)Lecture5.28
-
How to Choose KLecture5.29
-
How to Create Model (Code) + Further ReadingLecture5.30
-
อธิบาย KNN สำหรับ RegressionLecture5.31
-
PredictionLecture5.32
-
Workshop – AlzheimerLecture5.33
-
-
Day 6 30
-
Agenda – Day6Lecture6.1
-
Introduction to Naive BayesLecture6.2
-
What is Naive Bayes?Lecture6.3
-
Pros & ConsLecture6.4
-
Real World ApplicationLecture6.5
-
Assumption + Real Face of The ModelLecture6.6
-
How to Create Model (Math)Lecture6.7
-
How to Create Model (Code)Lecture6.8
-
PredictionLecture6.9
-
Workshop – Smart FarmLecture6.10
-
ี่ที่มาสมการ Naive BayesLecture6.11
-
มุมมองเพิ่มเติมเกี่ยวกับ Naive BayesLecture6.12
-
บทส่งท้าย Naive BayesLecture6.13
-
Introduction to SVCLecture6.14
-
What is SVC?Lecture6.15
-
Data for SVCLecture6.16
-
Pros & Cons + Real Face of the ModelLecture6.17
-
คำถามที่น่าสนใจ1Lecture6.18
-
Assumption + Real Face of the ModelLecture6.19
-
How to Create Model (Math) part1Lecture6.20
-
How to Create Model (Math) part2Lecture6.21
-
How to Create Model (Math) part3Lecture6.22
-
คำถามที่น่าสนใจ2Lecture6.23
-
How to Create Model (Code)Lecture6.24
-
Further ReadingLecture6.25
-
PredictionLecture6.26
-
Workshop – Sound ClassificationLecture6.27
-
ตอบคำถามนักเรียน1Lecture6.28
-
Support Vector RegressionLecture6.29
-
Workshop – Water RequirementLecture6.30
-
-
Day 7 21
-
Agenda – Day7Lecture7.1
-
ภาพจากมุมนอกของ GP & LDALecture7.2
-
What is Gaussian Process? part1Lecture7.3
-
What is Gaussian Process? part2Lecture7.4
-
GP – Pros & ConsLecture7.5
-
GP – Real World ApplicationsLecture7.6
-
Model Creation & PredictionLecture7.7
-
Workshop – Bike Rental part1Lecture7.8
-
Workshop – Bike Rental part2Lecture7.9
-
Workshop – ทดลอง GP กับ dataset อื่น ๆLecture7.10
-
What is Linear Discriminant Analysis?Lecture7.11
-
LDA – Assumption + Pros&ConsLecture7.12
-
Workshop – Credit Card ApprovalLecture7.13
-
Conclusion – AI OverviewLecture7.14
-
Conclusion – Data PreparationLecture7.15
-
Conclusion – Model EvaluationLecture7.16
-
Conclusion – 12 Algorithms part1Lecture7.17
-
Conclusion – 12 Algorithms part2Lecture7.18
-
Conclusion – 12 Algorithms part3Lecture7.19
-
ตอบคำถามนักเรียนเรื่อง Ensemble MethodLecture7.20
-
ปิดคอร์ส AI fo Serious BeginnersLecture7.21
-
Introduction
AI Overview – History of AI
Next