Hi there
, I'm Jay
ð 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 ð
ð Welcome to my GitHub Profile ð
ð Iâm currently studying on Artificial Intelligence, Machine Learning, Quantum Technology, A.I Cloud and Biology.
ð I am currently a third year student pursuing a BS in Computer Science with a specialization in Machine Learning at National University.
ð I'm planning to take a Master Degree for Artificial Intelligence or BioInformatics by the year of 2027
ð§? In addition to my studies at National University, I am also enrolled in the "CS50âs
ð§? Introduction to Artificial Intelligence with Python" course at Harvard University.
ð§ I have accumulated 8 years of experience as a Software Engineer and Blockchain Developer, starting from 2018.
ð¯ Iâm looking to collaborate on any Data Science, LLM and Web3 projects
ð¤ Iâm looking for help to work with Cloud Computing, Artificial Intelligence, Machine Learning, and Blockchain Development
ð¤ I would love to level-up my knowledge in BioInformatics, Cyber Security, Quantum Computing, Robotic Process Automation
ð± Iâm currently learning more about Rust, Go, Consensus Algorithm of Blockchain Technology and other Blockchain EVM
ð Iâm also exploring some revolutionary technology such as Web 4.0, Generative AI, IoT, Cloud Computing and Augmented Reality
𦾠Programming: I'm currently learning more on programming languages such as Python, R, Java & C++ so I can build and implement models.
ð Probability, statistics, and linear algebra: These are my math buddy needed to implement different AI and machine learning models.
ð§ Big data technologies: AI engineers work with large amounts of data, so Iâll be required to know Apache Spark, Hadoop, and MongoDB.
ð¤ Algorithms & frameworks: I'm currently self studying some machine learning algorithms such as linear regression and Naive Bayes,
ð¤ as well as deep learning algorithms such as recurrent neural networks and generative adversarial networks, and be able to implement
ð¤ them with a framework. Common AI frameworks include Theano, TensorFlow, Caffe, Keras, and PyTorch.
ð¬ Ask me about Artificial Intelligence and Machine Learning
ð® I'm a Dallas Mavericks fan since 2011, guess my idol ð¤«
Â?
ð Kindly visit my other GitHub profile: flexyledger for more content related to blockchain development
ð« How to reach me flexycode.dev@gmail.com, flexycode@protonmail.com, flexyledger@gmail.com
â¡Fun fact : I'm good at learning new things and adapting easily
â¡Fun fact : I always read and write documentation everyday before I begin to code
â¡Fun fact : I love Final Fantasy, Science Fiction, Biology, Architecture, Astrology, Mutants and Galaxy Adventure
â¡Fun fact : I also play League of Legends, Teamfight Tactics, Wild Rift, Legends of Runeterra, NBA2K
Â?Â?Â?
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React |
Python |
JavaScript |
C++ |
Webpack |
MySQL |
TypeScript |
AWS |
C# |
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Django |
Github |
Git |
Laravel |
HTML5 |
CSS |
Bootstrap |
Tailwind |
jQuery |
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MongoDB |
Nodejs |
PHP |
VsCode |
WordPress |
Vue |
Sass |
GraphQL |
PostgreSQL |
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| Certification | Verify | |
|---|---|---|
| ð | Legacy Full Stack | View |
| ð | Legacy Responsive Web Design V8 | View |
| ð | Legacy JavaScript Algorithms and Data Structures V7 | View |
| ð | Back End Development and APIs V8 | View |
| ð | Legacy Information Security and Quality Assurance | View |
| ð | Intro to Python for Data Science Course | View |
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ð¨ï¸ Technologies Icons :
| Flexycode | Flexyledger |
|---|---|
| â¡ï¸ ð Communication | â¡ï¸ ð§® Fortran |
| â¡ï¸ ð§° Version Control | â¡ï¸ âï¸ Erlang/Elixir |
| â¡ï¸ ð¨ Tools | â¡ï¸ 𧪠Testing |
| â¡ï¸ ð Web Dev | â¡ï¸ ð± Mobile Dev |
| â¡ï¸ ð JavaScript | â¡ï¸ ⨠UI/UX |
| â¡ï¸ â Java | â¡ï¸ ð§ Apache |
| â¡ï¸ Â©ï¸ C/C++ | â¡ï¸ ð® Game Development |
| â¡ï¸ ðª C# | â¡ï¸ ð¬ Analytics |
| â¡ï¸ ð Python | â¡ï¸ ð¤ AI |
| â¡ï¸ ð PHP | â¡ï¸ ð¾ Database |
| â¡ï¸ ð Ruby | â¡ï¸ âï¸ Cloud |
| â¡ï¸ 𦾠Rust | â¡ï¸ ð¥ï¸ Operating system |
| â¡ï¸ ð¿ï¸ Go | â¡ï¸ 𤿠DevOps |
| â¡ï¸ ð¼ How to use this icons? | â¡ï¸ ð¶ Contribution |
ð¨ï¸ Development Icons :
| Fullstack | Blockchain |
|---|---|
| â¡ï¸ â¡ Next.js 15 | â¡ï¸ ð§© Component Library |
| â¡ï¸ ð¨ Tailwind CSS | â¡ï¸ ð® AI Playground |
| â¡ï¸ ð TypeScript | â¡ï¸ ð Dashboard Template |
| â¡ï¸ ð Authentication | â¡ï¸ ð SEO Optimized |
| â¡ï¸ ð Shadcn/ui | â¡ï¸ ⨠UI/UX |
| â¡ï¸ ð¾ Convex DB | â¡ï¸ ð¬ Custom Video Player |
| â¡ï¸ ð³ Polar.sh | â¡ï¸ ð® Game Development |
| â¡ï¸ ð Route Prefetching | â¡ï¸ ð Blog Support |
| â¡ï¸ ð¼ï¸ Optimized Images | â¡ï¸ ð State Management |
| â¡ï¸ ð Dark/Light Mode | â¡ï¸ ð¾ Database |
| â¡ï¸ ð± Responsive Design | â¡ï¸ âï¸ Cloud |
| â¡ï¸ ð¾ State Persistence | â¡ï¸ ð API Integration |
| â¡ï¸ ð Real-time Updates | â¡ï¸ 𤿠DevOps |
| â¡ï¸ ð¼ How to use this icons? | â¡ï¸ ð¶ Contribution |
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# Import the necessary libraries for AI
import numpy as np
import pandas as pdÂ?
import tensorflow as tfÂ?
# 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)Â?Â?
# Use the AI model for predictions
predictions = model.predict(X_test)Â?
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# 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')Â?
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)
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