Machine Learning Data Science AI

HUYNH BA THIEN

Ho Chi Minh City ******.*****@*****.*** +84-377****** LinkedIn Github

OBJECTIVE

Seeking an AI Engineer Internship position to apply and enhance my skills in artificial intelligence, machine learning, and data analysis in a practical setting. Eager to contribute to a dynamic team, gain hands-on experience with AI-driven projects, and support the development of innovative solutions that drive business success. Committed to advancing my knowledge in AI engineering and analytics through practical application and collaboration with industry professionals.

SKILLS

• Programming:

• S/w & tools:

• Language:

• Framework:

Python, C/C++, Matlab, SQL (SQL server), R

Microsoft Office, Linux, Latex, Git, Docker

English: Professional Working Proficiency, VSTEP: 7.0/10 (23/08/2023) Numpy, Pandas, Matplotlib, Scikit-learn, NLTK, OpenCV, PyTorch, Apache Airflow, TensorFlow, HuggingFace, LangChain

EDUCATION

University of Science Bachelor of Data Science District 5, HCM city 09.2020 – Present

• Cumulative GPA: 3.21/4.0

• Subjects: Calculus, Linear Algebra, Probability and Statistics, Discrete Mathematics, Combinatorial Mathematics and Discrete Structures, Mathematical Statistics, Object Oriented Programming, Data Structure and Algorithm, Python for Data Science, Artificial intelligence, Data Mining, Machine Learning, Multivariate Statistical Analysis, Linear Programming, Database Management System, Deep Learning EXPERIENCE

University courses:

Python for Data Science: Applied Python for data science, utilizing Pandas for diverse data sources, preprocessing data, and employing NumPy for advanced data manipulation. Demonstrated proficiency in data visualization using Matplotlib, and Seaborn. Successfully applied Scikit-Learn for machine learning tasks, culminating in the comprehensive use of machine learning methods to solve real-world problems in the final report at the end of the semester.

Artificial Intelligence: Gained a foundational understanding of AI concepts and algorithms, including DFS, BFS, UCS, GBFS, and A-star. Introduced to symbolic programming using Prolog.

Data Mining: Acquired in-depth knowledge of the data mining process, various data types, and practical experience in labs covering data preparation, similarity measures, association pattern mining, cluster analysis, and data classification. Machine Learning: Mastered theoretical concepts and applied them in practical labs covering linear regression, logistic regression, Naive Bayes classification, decision trees, Unsupervised K-means, SVM, and basic neural network architectures in deep learning. Multivariate Statistical Analysis: Applied multivariate analysis techniques using R, including multivariate normal distribution, principal components, and factor analysis.

Database Management System: Proficiently learned and applied advanced SQL syntax in SQL Server, encompassing Functions, Triggers, Transactions, Cursors, and Stored procedures, and ventured into Non-SQL databases such as MongoDB. Deep Learning: Understanding network layers, learning how to perform Backpropagation, and training a deep neural network. Exploring various optimizers such as SGD, SGD with momentum, AdaGrad, RMSProp, and Adam. Additionally, gaining insights into popular network architectures like LeNet, AlexNet, VGG16, Inception v1, ResNet, DenseNet, Unet, and MobileNet v1. Lastly, delving into Recurrent Neural Networks (RNN) and reinforcing knowledge through practice labs

Natural Language Processing Seminar (Progressing): Researching Language Model (LLM)-powered Autonomous Agents, applying deep tech like QLoRA, and fine-tuning pre-trained models such as LoRA. Gaining a basic understanding of reinforcement learning with human feedback. Exploring Direct Preference Optimization in Zephyr 7B.

Online courses:

Coursera Machine Learning Specialization DeepLearning.AI 08.2022

• Supervised Machine Learning: Regression and Classification

• Advanced Learning Algorithms

• Reinforcement Learning

Coursera Natural Language Processing with Classification and Vector Spaces DeepLearning.AI 10.2023 INTERESTS

• Football: Avid player and enthusiast, actively participating in football to stay physically active and engaged with the sport.

• Calisthenics: Committed to a healthy lifestyle through calisthenics, emphasizing bodyweight exercises for strength and flexibility.

• Board Games: Find enjoyment in interacting with friends through board games, fostering social connections and friendly competition.

• Learning Languages: Actively engaged in studying different languages around the world, demonstrating a curiosity for diverse cultures and linguistic nuances.

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