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 (70 Hours) [เรียนฟรี แค่สมัครสมาชิก!!!]

Courses 23	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
22	AI Ethics		Basic for Python Programing
23	Software License		Basic for Python Programing

Intermediate Level (180 Hours)

Courses 51	Courses	Prerequisite course
1	Advanced to Mathematics for AI	Mathematics for AI
2	Advanced to Classical Machine Learning	Introduction to Classical Machine Learning
3	Advanced to Neural Network and Deep Learning	Introduction to Neural Network and DL
4	Introduction to Point Cloud (Digital Geometry Processing)	Introduction to Image processing and CV
5	Introduction to Point Cloud (2D-3D Reconstruction)	Digital Geometry Processing
6	Introduction to Point Cloud (3D Deep Learning and PointNet)	2D-3D Reconstruction
7	One-Class-Classificaion	Introduction to Image processing and CV
8	Principal Component Analysis (PCA)	Mathematics for AI
9	Hidden Markov Model (HMM)	Mathematics for AI
10	Acoustic Speech Recognition (ASR)	Hidden Markov Model
11	Natural Language Processing (Computational linguistics)	Introduction to Natural Language Processing
12	Natural Language Processing (Automata)	Introduction to Natural Language Processing
13	Natural Language Processing (Text Processing)	Introduction to Natural Language Processing
14	Natural Language Processing (Question-Answering)	Introduction to Natural Language Processing
15	Natural Language Processing (Machine Translation)	Introduction to Natural Language Processing
16	Natural Language Processing (Web Scraping and Document Classification)	Introduction to Natural Language Processing
17	Stock Manipulation (Recurrent Neural Network)	Introduction to Data Science
18	Finance Focus on Reinforcement Learning	Introduction to Neural Network and DL
19	Advanced to Internet of Things	Introduction to Internet of Things
20	Advanced to Robotics (Robot Kinematic)	Introduction to Robotics
21	Introduction to Deployment	Introduction to Linux
22	Introduction to Docker	Introduction to Linux
23	Microservices and Docker Compose	Introduction to Linux
24	Advanced Ensemble Methods	Ensemble Method
25	Advanced Image Processing	Introduction to Image processing and Computer vision
26	Data Science and Statistics	Introduction to Data Science
27	Data Analytics for Signal Processing	Introduction to Data Science
28	Stock Analysis	Introduction to Data Science
29	Image Processing and Deep Learning	Basic for Python Programing
30	Image Processing for Tutorial and Workshop	Image Processing and Deep Learning
31	Kaggle Tutorial and Workshop	Introduction to Kaggle
32	NLP Basic Tools and Their Application	Introduction to Natural Language Processing
33	NLP Applications and Ideation	Introduction to Natural Language Processing
34	NLP for Law	Introduction to Natural Language Processing
35	NLP Basic Tools and Their Application (Text to Speech: TTS)	Introduction to Natural Language Processing
36	Machine Translation and The BERT Family	Natural Language Processing (Machine Translation)
37	Virtual Influencer	Computer Vision
38	Artificial Intelligence and Fault Diagnosis of Electric Motors	Introduction to Signal Processing
39	Introduction to BCI and BCI-Controlled Robot	Introduction to Signal Processing
40	Motor Imagery EEG-BCI	Introduction to Signal Processing
41	Comparing Test Sets with Item Response Theory	Introduction to Natural Language Processing
42	Learning in Machines Translation and Brains	Introduction to Natural Language Processing
43	Stock Analysis : Portfolio Optimization	Introduction to Data Science
44	Advanced Natural Language Processing	Introduction to Natural Language Processing
45	Advanced NLP for Tutorial and Workshop	Introduction to Natural Language Processing
46	Word2Vec	Introduction to Natural Language Processing
47	Machine Translation for Tranformer	Introduction to Natural Language Processing
48	Machine Translation workshop	Introduction to Natural Language Processing
49	Internet of Things and Robotics	Introduction to Internet of Things
50	IoT and Robotics for Tutorial and Workshop	Internet of Things and Robotics
51	Blockchain Smart Contract Tutorial and Workshop	Basic for Python Programing

Applied Level (Hackathon Level 2 only)

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