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💎 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 💎

Coding

💎 Welcome to my GitHub Profile 💎

🔭 I’m currently studying on Artificial Intelligence, Machine Learning, Quantum Technology, A.I Cloud and Biology.
📓 I am currently a third 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 flexycode.dev@gmail.com, flexycode@protonmail.com, flexyledger@gmail.com

# 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) 

# 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)