หลักสูตร Super AI Engineer เป็นส่วนหนึ่งของโครงการ Super AI Engineer ทำการสอน AI เป็นภาษาไทย (เนื้อหาเป็นภาษาอังกฤษ)

Fundamental Level เปิดให้ผู้ที่สนใจเข้าเรียนได้ฟรี เพียงทำการสมัครสมาชิกและทำการ Enroll รายวิชา ในส่วนของ Intermediate Level สำหรับผู้สมัครโครงการ Super AI Engineer เท่านั้น

Fundamental Level (60 Hours) [เรียนฟรี แค่สมัครสมาชิก!!!]

Courses
21
Courses   Prerequisite course
1 Basic for Python Programing  
2 Introduction to Linux  
3 Introduction to Kaggle   Basic for Python Programing
4 Introduction to Google Colab   Basic for Python Programing
5 Introduction to Github   Basic for Python Programing
6 Mathematics for AI  
7 Introduction to Classical Machine Learning   Mathematics for AI
8 Ensemble Method   Mathematics for AI
9 Introduction to Neural Network and Deep Learning   Mathematics for AI
10 Introduction to Natural Language Processing   Basic for Python Programing
11 Introduction to Image processing and Computer vision   Basic for Python Programing
12 Introduction to Internet of Things   Basic for Python Programing
13 Introduction to Robotics   Basic for Python Programing
14 Computer Vision   Basic for Python Programing
15 Introduction to Signal Processing   Basic for Python Programing
16 Introduction to Signal Processing for Tutorial and Workshop   Basic for Python Programing
17 Introduction to Data Science   Basic for Python Programing
18 Introduction to Genetic Algorithms   Basic for Python Programing
19 Introduction to Data Visualization   Basic for Python Programing
20 Introduction to Data Science and Big Data   Basic for Python Programing
21 Predicate logic and Artificial Intelligence   Basic for Python Programing

Intermediate Level (160 Hours)

Courses
48
CoursesPrerequisite course
1Advanced to Mathematics for AIMathematics for AI
2Advanced to Classical Machine LearningIntroduction to Classical Machine Learning
3Advanced to Neural Network and Deep LearningIntroduction to Neural Network and DL
4Introduction to Point Cloud (Digital Geometry Processing)Introduction to Image processing and CV
5Introduction to Point Cloud (2D-3D Reconstruction)Digital Geometry Processing
6Introduction to Point Cloud (3D Deep Learning and PointNet)2D-3D Reconstruction
7One-Class-ClassificaionIntroduction to Image processing and CV
8Principal Component Analysis (PCA)Mathematics for AI
9Hidden Markov Model (HMM)Mathematics for AI
10Acoustic Speech Recognition (ASR)Hidden Markov Model
11Natural Language Processing (Computational linguistics)Introduction to Natural Language Processing
12Natural Language Processing (Automata)Introduction to Natural Language Processing
13Natural Language Processing (Text Processing)Introduction to Natural Language Processing
14Natural Language Processing (Question-Answering)Introduction to Natural Language Processing
15Natural Language Processing (Machine Translation)Introduction to Natural Language Processing
16Natural Language Processing (Web Scraping and Document Classification)Introduction to Natural Language Processing
17Stock Manipulation (Recurrent Neural Network)Introduction to Data Science
18Finance Focus on Reinforcement LearningIntroduction to Neural Network and DL
19Advanced to Internet of ThingsIntroduction to Internet of Things
20Advanced to Robotics (Robot Kinematic)Introduction to Robotics
21Introduction to DeploymentIntroduction to Linux
22Introduction to DockerIntroduction to Linux
23Microservices and Docker ComposeIntroduction to Linux
24Advanced Ensemble MethodsEnsemble Method
25Advanced Image ProcessingIntroduction to Image processing and Computer vision
26Data Science and StatisticsIntroduction to Data Science
27Data Analytics for Signal ProcessingIntroduction to Data Science
28Stock AnalysisIntroduction to Data Science
29Image Processing and Deep LearningBasic for Python Programing
30Image Processing for Tutorial and WorkshopImage Processing and Deep Learning
31Kaggle Tutorial and WorkshopIntroduction to Kaggle
32NLP Basic Tools and Their ApplicationIntroduction to Natural Language Processing
33NLP Applications and IdeationIntroduction to Natural Language Processing
34NLP for LawIntroduction to Natural Language Processing
35NLP Basic Tools and Their Application (Text to Speech: TTS)Introduction to Natural Language Processing
36Machine Translation and The BERT FamilyNatural Language Processing (Machine Translation)
37Virtual InfluencerComputer Vision
38Artificial Intelligence and Fault Diagnosis of Electric MotorsIntroduction to Signal Processing
39Introduction to BCI and BCI-Controlled RobotIntroduction to Signal Processing
40Motor Imagery EEG-BCIIntroduction to Signal Processing
41Comparing Test Sets with Item Response TheoryIntroduction to Natural Language Processing
42Learning in Machines Translation and BrainsIntroduction to Natural Language Processing
43Stock Analysis : Portfolio OptimizationIntroduction to Data Science
44Advanced Natural Language ProcessingIntroduction to Natural Language Processing
45Advanced NLP for Tutorial and WorkshopIntroduction to Natural Language Processing
46Word2VecIntroduction to Natural Language Processing
47Machine Translation for TranformerIntroduction to Natural Language Processing
48Machine Translation workshopIntroduction to Natural Language Processing

Applied Level (Hackathon Level 2 only)

Courses
6
Courses Prerequisite course
1Applied to Natural Language Processing
2Applied to Image Processing
3Applied to Signal Processing
4Applied to Data Science
5Applied to Internet of Thing
6Applied to Robotics