data-ai-technologies

name	data-ai-technologies
description	Master data science and AI with machine learning, deep learning, and LLMs. Use when building ML models, working with data, or implementing AI features.

Data Science & AI Technologies Skill

Comprehensive guide to data science and AI development.

Quick Start

Python Data Analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data.csv')

# Data exploration
print(df.head())
print(df.describe())
print(df.isnull().sum())

# Data cleaning
df = df.dropna()
df['age'] = pd.to_numeric(df['age'], errors='coerce')

# Grouping and aggregation
summary = df.groupby('category')['sales'].agg(['mean', 'sum', 'count'])

# Visualization
plt.figure(figsize=(10, 6))
plt.scatter(df['age'], df['income'])
plt.xlabel('Age')
plt.ylabel('Income')
plt.show()

Machine Learning with scikit-learn

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

# Prepare data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Evaluate
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2%}")

Deep Learning with PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

model = SimpleNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
for epoch in range(10):
    for batch_x, batch_y in train_loader:
        outputs = model(batch_x)
        loss = criterion(outputs, batch_y)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

Using LLMs

from openai import OpenAI

client = OpenAI(api_key="sk-...")

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain machine learning in simple terms."}
    ],
    temperature=0.7,
    max_tokens=200
)

print(response.choices[0].message.content)

Key Concepts

Data Processing

• Data cleaning and preprocessing
• Feature engineering
• Data normalization and scaling
• Handling missing values
• Outlier detection
• Dimensionality reduction

Machine Learning

• Supervised vs unsupervised learning
• Classification and regression
• Hyperparameter tuning
• Cross-validation
• Overfitting prevention
• Model selection

Deep Learning

• Neural network architecture
• Activation functions
• Backpropagation
• Convolutional networks (CNN)
• Recurrent networks (RNN, LSTM)
• Transfer learning
• Fine-tuning pre-trained models

Large Language Models

• Transformer architecture
• Tokenization
• Embeddings
• Prompt engineering
• RAG (Retrieval Augmented Generation)
• Fine-tuning LLMs
• Cost optimization

Evaluation Metrics

• Accuracy, Precision, Recall, F1
• ROC-AUC curve
• Confusion matrix
• Mean squared error (MSE)
• Mean absolute error (MAE)
• Perplexity for language models

Popular Libraries

Data Processing

• Pandas: Data manipulation
• NumPy: Numerical computing
• Polars: Fast data processing
• DuckDB: SQL for data analysis

Machine Learning

• scikit-learn: Classical ML
• XGBoost: Gradient boosting
• LightGBM: Fast boosting
• CatBoost: Categorical boosting

Deep Learning

• PyTorch: Flexible framework
• TensorFlow: Production framework
• JAX: Numerical computing
• Hugging Face: Transformers library

Data Visualization

• Matplotlib: Static plots
• Seaborn: Statistical visualizations
• Plotly: Interactive plots
• Altair: Declarative visualization

Resources

• Scikit-learn Documentation
• PyTorch Tutorials
• Hugging Face Course
• Fast.ai Courses
• Kaggle Datasets