data-science-ai

name	data-science-ai
description	Master machine learning, data engineering, AI/LLM development, and MLOps. Use when building data-driven applications, implementing ML models, or managing ML pipelines.

Data Science & AI Engineering

Quick Start

Data Science and AI Engineering focus on data analysis, machine learning, and artificial intelligence. The roadmap covers:

Core Competencies

• Languages: Python, R, SQL, Java, Scala
• ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
• Data Tools: Pandas, NumPy, Matplotlib, Seaborn
• Data Engineering: Airflow, Spark, Kafka, Hadoop
• Databases: PostgreSQL, MongoDB, BigQuery, Snowflake
• Cloud: AWS, Azure, Google Cloud
• AI/LLMs: OpenAI, Hugging Face, Ollama, Claude
• MLOps: MLflow, Kubeflow, DVC

ML Fundamentals

Machine Learning Pipeline:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

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

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y)

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

# Evaluate
accuracy = model.score(X_test, y_test)

Deep Learning with PyTorch:

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(10, 64)
        self.fc2 = nn.Linear(64, 1)

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

Prompt Engineering for LLMs:

from openai import OpenAI

client = OpenAI()

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."}
    ]
)

Study Path

1. Python Fundamentals (1-2 weeks): Core language and libraries
2. Data Analysis (2-3 weeks): Pandas, NumPy, visualization
3. Machine Learning (3-4 weeks): Scikit-learn, algorithms, evaluation
4. Deep Learning (2-3 weeks): Neural networks, TensorFlow/PyTorch
5. AI/LLMs (1-2 weeks): Prompt engineering, fine-tuning
6. MLOps (2-3 weeks): Model deployment, monitoring, pipelines

Key Topics

• Data collection and preprocessing
• Exploratory data analysis (EDA)
• Feature engineering
• Model selection and training
• Hyperparameter tuning
• Evaluation metrics and validation
• Deep learning architectures
• Prompt engineering techniques
• Model deployment and serving
• MLOps and pipelines

Resources

• Developer Roadmap: https://roadmap.sh/machine-learning
• TensorFlow: https://www.tensorflow.org
• PyTorch: https://pytorch.org
• Scikit-learn: https://scikit-learn.org
• OpenAI: https://openai.com/api