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Name: Thanapoom Phatthanaphan
Type: User
Bio: Interested in Programming, Data Science, Machine Learning, Artificial Intelligence
Thanapoom Phatthanaphan's Projects
bia660_web-mining
Learned techniques and tools for Web Mining and Natural Language Processing: BeautifulSoup, Selenium, Regular Expression, Text Classification, Text Clustering
churn-predictions-telecom-customer
Developed a churn prediction classification model using various techniques including: EDA, Decision trees, Naive Bayes, AdaBoost, MLP, Bagging, RF, KNN, logistic regression, SVM, Hyperparameter tuning using Grid Search CV and Randomized Search CV.
cs501_introduction-java-programming
Learned basic Java Programming: Data types, Flow of control, Classes, Methods and Objects, Arrays, Exception Handling, and Recursion.
cs513_knowledge-discovery-data-mining
Learned techniques and tools for Knowledge Discovery and Data Mining: R, RStudio, Classification Models
cs515_fundamentals-computing
Learned basic Python programming: Program design, Algorithmic thinking, Recursion, Object-oriented programming, Interpreters, Compilers, and Data representation
cs541_artificial-intelligence
Learned both learning and problem solving to develop statistical models for real-world AI applications
cs550_computer-organization-programming
Learned Computer organization and Assembly programming: Structure of program computer, Linking and Loading, Translation of high-level language, Logic design, Processor design, Data path, Hardwired control, Microprogrammed control
cs556_mathematical-foundations-machine-learning
Learned the fundamentals of mathematic and some techniques in machine learning: Linear Algebra, Calculus, Probability, Linear Regression, Support Vector Machine
cs559_machine-learning-fundamentals-applications
Learned the fundamentals and applications in ML: Intro to Prob. & Linear algebra, Decision Theory, MLE & BE, Linear Model, Linear Discriminant function, Perceptron, FLD, PCA, Non-parametric Learning, Clustering, EM, GMM, EM and Latent Variable Model, Probabilistic Graphical Model, Bayesian Network, Neural Network, SVM, Decision Tree and Boosting
cs561_database-management-system-i
Learned the fundamental concepts of database management systems, emphasizing relational databases in both theory and practice.
cs570_datastructures-algorithms
Learned Data Structures and Algorithms: Basic Programming Constructs, Data types, Search trees, Hashing, Complexity Analysis, Algorithm design, Graph algorithms, Sort algorithms
cs583_deep-learning
Learned knowledge and techniques in Deep Learning and also related tools: Python, Pytorch, Jupyter Notebook, RNN, CNN, Reinforcement Learning, LSTM, BERT, Language Modeling
cs584_natural-language-processing
Learned knowledge and techniques in Natural Language Processing and also related tools: Python, Pytorch, Jupyter Notebook, Google Colab, RNN, CNN, Reinforcement Learning, LSTM, Language Modeling
movies-recommendation-hybrid-system
Developed a Hybrid movies recommendation system, using various techniques including: Collaborative Filtering, Content-Based Filtering, Singular Value Decomposition, Min-Max Normalization, Cosine Similarity, Linear Regression Model
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