💎 Hello there! I'm thrilled to have you visit my GitHub profile. Here, you'll find a collection of my projects, contributions, and explorations in the world of software development 💎

 

 

 

💎 Welcome to my GitHub Profile 💎

🔭 I’m currently studying on Artificial Intelligence, Machine Learning, Quantum Technology and Biology
📓 I am currently a second-year student pursuing a BS in Computer Science with a specialization in Machine Learning at National University.
📓 I'm planning to take a Master Degree for Artificial Intelligence or BioInformatics by the year of 2027
🧠 In addition to my studies at National University, I am also enrolled in the "CS50’s
🧠 Introduction to Artificial Intelligence with Python" course at Harvard University.
🧊 I have accumulated 8 years of experience as a Software Engineer and Blockchain Developer, starting from 2018.
👯 I’m looking to collaborate on any Data Science, LLM and Web3 projects
🤝 I’m looking for help to work with Cloud Computing, Artificial Intelligence, Machine Learning, and Blockchain Development
🤝 I would love to level-up my knowledge in BioInformatics, Cyber Security, Quantum Computing, Robotic Process Automation
🌱 I’m currently learning more about Rust, Go, Consensus Algorithm of Blockchain Technology and other Blockchain EVM
🌐 I’m also exploring some revolutionary technology such as Web 4.0, Generative AI, IoT, Cloud Computing and Augmented Reality
🦾 Programming: I'm currently learning more on programming languages such as Python, R, Java & C++ so I can build and implement models.
📈 Probability, statistics, and linear algebra: These are my math buddy needed to implement different AI and machine learning models.
🧊 Big data technologies: AI engineers work with large amounts of data, so I’ll be required to know Apache Spark, Hadoop, and MongoDB.
🤖 Algorithms & frameworks: I'm currently self studying some machine learning algorithms such as linear regression and Naive Bayes,
🤖 as well as deep learning algorithms such as recurrent neural networks and generative adversarial networks, and be able to implement
🤖 them with a framework. Common AI frameworks include Theano, TensorFlow, Caffe, Keras, and PyTorch.
💬 Ask me about Artificial Intelligence and Machine Learning
🎮 I'm a Dallas Mavericks fan since 2011, guess my idol 🤫
 

🌐 Kindly visit my other GitHub profile: flexyledger for more content related to blockchain development
📫 How to reach me [email protected], [email protected], [email protected]

Connect with me on  

Languages & Tools  

 

💻 My favorite tools and technologies for Full Stack Devevelopment

React	Python	JavaScript	C++	Webpack	MySQL	TypeScript	AWS	C#
Django	Github	Git	Laravel	HTML5	CSS	Bootstrap	Tailwind	jQuery
MongoDB	Nodejs	PHP	VsCode	WordPress	Vue	Sass	GraphQL	PostgreSQL

💻 My tools and technologies for Blockchain Devevelopment

💻 My tools and technologies for AI and ML Devevelopment

   

💻 My tools and technologies for Game Devevelopment, Architecture and Visualization

     

💻 Some Technology Stacks that I used:

                   

📈 GitHub Contribution:

 

My GitHub contributions graph

GitHub Stats  

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

🏆 GitHub Trophies

🏆 GitHub Repository

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

Icons for Commiting message

Icons
♻️ - refactor getGithubUserContribution
✨ - read contribution calendar from github api or remove some attributes
📓 - commit or add readme
👷 - add manual run, repair
🚑 - import or also commit
🔨 - fix algorithm priority
🚀 - add emojis and style
🤫 - smiley face can also use for indicator for running or stopping some container
⛓️ - for linking file or repo
💱 - using solidity, hardhat or crypto related function
🧊 - blockchain
🌐 - networking setting, YAML file
📋 - List of Content

Some Programming Language & Technologies Icons for Commiting message

🖨️ Technologies Icons :

Flexycode	Flexyledger
➡️ 🚍 Communication	➡️ 🧮 Fortran
➡️ 🧰 Version Control	➡️ ☎️ Erlang/Elixir
➡️ 🔨 Tools	➡️ 🧪 Testing
➡️ 🌐 Web Dev	➡️ 📱 Mobile Dev
➡️ 📜 JavaScript	➡️ ✨ UI/UX
➡️ ☕ Java	➡️ 🧊 Apache
➡️ ©️ C/C++	➡️ 🎮 Game Development
➡️ 🪒 C#	➡️ 🔬 Analytics
➡️ 🐍 Python	➡️ 🤖 AI
➡️ 🐘 PHP	➡️ 💾 Database
➡️ 💎 Ruby	➡️ ☁️ Cloud
➡️ 🦾 Rust	➡️ 🖥️ Operating system
➡️ 🐿️ Go	➡️ 🤿 DevOps
➡️ 🍼 How to use this icons?	➡️ 🚶 Contribution

✍️ Random Dev Quote 

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

🧠🧠 Artificial Intelligence 🛸🛸

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️

# Import the necessary libraries for AI
import numpy as np
import pandas as pd 
import tensorflow as tf 

# Define the AI model architecture
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu', input_dim=10))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

# Compile and train the AI model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)  

# Use the AI model for predictions
predictions = model.predict(X_test) 

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🌐⛓️ Blockchain Technology 💱🧊  

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🤖🦾 Machine Learning 📈💡

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

# Import the necessary libraries for ML
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset
data = pd.read_csv('data.csv') 
X = data.drop('target', axis=1)
y = data['target']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
predictions = model.predict(X_test)

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, predictions)

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️  ♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️ 

🔑🔐 Cryptography and Cybersecurity 🔒🕵️   

♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️♾️  

🌐🌐 Quantum Technology 🌐🌐

Back to Table of Content

---